{"item_id": "TOOL-pricing_wtp-e-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 61\n - In-store intercept rating: 73\n - Trade-off analysis index: 44\n - Retention predictor score: 42\n - Benchmark comparison rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 42.0, \"max\": 73.0, \"std\": 11.43}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 45, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 61\n - In-store intercept rating: 73\n - Trade-off analysis index: 44\n - Retention predictor score: 42\n - Benchmark comparison rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 42.0, \"max\": 73.0, \"std\": 11.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 61\n - In-store intercept rating: 73\n - Trade-off analysis index: 44\n - Retention predictor score: 42\n - Benchmark comparison rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 42.0, \"max\": 73.0, \"std\": 11.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 45, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 61\n - In-store intercept rating: 73\n - Trade-off analysis index: 44\n - Retention predictor score: 42\n - Benchmark comparison rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 42.0, \"max\": 73.0, \"std\": 11.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 61\n - In-store intercept rating: 73\n - Trade-off analysis index: 44\n - Retention predictor score: 42\n - Benchmark comparison rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 42.0, \"max\": 73.0, \"std\": 11.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 52\n - Van Westendorp index: 75\n - Gabor-Granger rating: 62\n - Choice-based conjoint score: 69\n - Expert panel assessment: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.2, \"count\": 5, \"min\": 52.0, \"max\": 75.0, \"std\": 8.08}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 52\n - Van Westendorp index: 75\n - Gabor-Granger rating: 62\n - Choice-based conjoint score: 69\n - Expert panel assessment: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.2, \"count\": 5, \"min\": 52.0, \"max\": 75.0, \"std\": 8.08}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 76, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 52\n - Van Westendorp index: 75\n - Gabor-Granger rating: 62\n - Choice-based conjoint score: 69\n - Expert panel assessment: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.2, \"count\": 5, \"min\": 52.0, \"max\": 75.0, \"std\": 8.08}\n\nTool: check_external_reference\nOutput: {\"request_id\": 76, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 52\n - Van Westendorp index: 75\n - Gabor-Granger rating: 62\n - Choice-based conjoint score: 69\n - Expert panel assessment: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.2, \"count\": 5, \"min\": 52.0, \"max\": 75.0, \"std\": 8.08}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 76, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 52\n - Van Westendorp index: 75\n - Gabor-Granger rating: 62\n - Choice-based conjoint score: 69\n - Expert panel assessment: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.2, \"count\": 5, \"min\": 52.0, \"max\": 75.0, \"std\": 8.08}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 76, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 49\n - Customer segment B survey score: 55\n - Focus group rating: 53\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 49.0, \"max\": 59.0, \"std\": 3.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 48, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 49\n - Customer segment B survey score: 55\n - Focus group rating: 53\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 49.0, \"max\": 59.0, \"std\": 3.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 48, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 49\n - Customer segment B survey score: 55\n - Focus group rating: 53\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 49.0, \"max\": 59.0, \"std\": 3.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 48, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 49\n - Customer segment B survey score: 55\n - Focus group rating: 53\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 49.0, \"max\": 59.0, \"std\": 3.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 48, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 49\n - Customer segment B survey score: 55\n - Focus group rating: 53\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 49.0, \"max\": 59.0, \"std\": 3.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 25\n - In-store intercept rating: 24\n - Trade-off analysis index: 43\n - Retention predictor score: 33\n - Benchmark comparison rating: 26\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.2, \"count\": 5, \"min\": 24.0, \"max\": 43.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 25\n - In-store intercept rating: 24\n - Trade-off analysis index: 43\n - Retention predictor score: 33\n - Benchmark comparison rating: 26\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.2, \"count\": 5, \"min\": 24.0, \"max\": 43.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 49, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 25\n - In-store intercept rating: 24\n - Trade-off analysis index: 43\n - Retention predictor score: 33\n - Benchmark comparison rating: 26\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.2, \"count\": 5, \"min\": 24.0, \"max\": 43.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"request_id\": 49, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 25\n - In-store intercept rating: 24\n - Trade-off analysis index: 43\n - Retention predictor score: 33\n - Benchmark comparison rating: 26\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.2, \"count\": 5, \"min\": 24.0, \"max\": 43.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 49, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 25\n - In-store intercept rating: 24\n - Trade-off analysis index: 43\n - Retention predictor score: 33\n - Benchmark comparison rating: 26\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.2, \"count\": 5, \"min\": 24.0, \"max\": 43.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 49, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 31\n - Van Westendorp index: 39\n - Gabor-Granger rating: 33\n - Choice-based conjoint score: 39\n - Expert panel assessment: 8\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.0, \"count\": 5, \"min\": 8.0, \"max\": 39.0, \"std\": 11.45}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 31\n - Van Westendorp index: 39\n - Gabor-Granger rating: 33\n - Choice-based conjoint score: 39\n - Expert panel assessment: 8\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.0, \"count\": 5, \"min\": 8.0, \"max\": 39.0, \"std\": 11.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 53, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 31\n - Van Westendorp index: 39\n - Gabor-Granger rating: 33\n - Choice-based conjoint score: 39\n - Expert panel assessment: 8\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.0, \"count\": 5, \"min\": 8.0, \"max\": 39.0, \"std\": 11.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 53, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 31\n - Van Westendorp index: 39\n - Gabor-Granger rating: 33\n - Choice-based conjoint score: 39\n - Expert panel assessment: 8\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.0, \"count\": 5, \"min\": 8.0, \"max\": 39.0, \"std\": 11.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 53, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 31\n - Van Westendorp index: 39\n - Gabor-Granger rating: 33\n - Choice-based conjoint score: 39\n - Expert panel assessment: 8\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 30.0, \"count\": 5, \"min\": 8.0, \"max\": 39.0, \"std\": 11.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 53, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 34\n - Customer segment B survey score: 26\n - Focus group rating: 44\n - Conjoint analysis index: 20\n - Historical price-sensitivity score: 23\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.4, \"count\": 5, \"min\": 20.0, \"max\": 44.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 34\n - Customer segment B survey score: 26\n - Focus group rating: 44\n - Conjoint analysis index: 20\n - Historical price-sensitivity score: 23\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.4, \"count\": 5, \"min\": 20.0, \"max\": 44.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 34\n - Customer segment B survey score: 26\n - Focus group rating: 44\n - Conjoint analysis index: 20\n - Historical price-sensitivity score: 23\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.4, \"count\": 5, \"min\": 20.0, \"max\": 44.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 34\n - Customer segment B survey score: 26\n - Focus group rating: 44\n - Conjoint analysis index: 20\n - Historical price-sensitivity score: 23\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.4, \"count\": 5, \"min\": 20.0, \"max\": 44.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 34\n - Customer segment B survey score: 26\n - Focus group rating: 44\n - Conjoint analysis index: 20\n - Historical price-sensitivity score: 23\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.4, \"count\": 5, \"min\": 20.0, \"max\": 44.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 79\n - In-store intercept rating: 73\n - Trade-off analysis index: 71\n - Retention predictor score: 54\n - Benchmark comparison rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.8, \"count\": 5, \"min\": 54.0, \"max\": 79.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 49, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 79\n - In-store intercept rating: 73\n - Trade-off analysis index: 71\n - Retention predictor score: 54\n - Benchmark comparison rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.8, \"count\": 5, \"min\": 54.0, \"max\": 79.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 49, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 79, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 79\n - In-store intercept rating: 73\n - Trade-off analysis index: 71\n - Retention predictor score: 54\n - Benchmark comparison rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.8, \"count\": 5, \"min\": 54.0, \"max\": 79.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 79, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 49, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 79\n - In-store intercept rating: 73\n - Trade-off analysis index: 71\n - Retention predictor score: 54\n - Benchmark comparison rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.8, \"count\": 5, \"min\": 54.0, \"max\": 79.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 49, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 79, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 79\n - In-store intercept rating: 73\n - Trade-off analysis index: 71\n - Retention predictor score: 54\n - Benchmark comparison rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.8, \"count\": 5, \"min\": 54.0, \"max\": 79.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 79, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 48\n - Van Westendorp index: 54\n - Gabor-Granger rating: 46\n - Choice-based conjoint score: 37\n - Expert panel assessment: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.8, \"count\": 5, \"min\": 37.0, \"max\": 54.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 48\n - Van Westendorp index: 54\n - Gabor-Granger rating: 46\n - Choice-based conjoint score: 37\n - Expert panel assessment: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.8, \"count\": 5, \"min\": 37.0, \"max\": 54.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 60, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 48\n - Van Westendorp index: 54\n - Gabor-Granger rating: 46\n - Choice-based conjoint score: 37\n - Expert panel assessment: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.8, \"count\": 5, \"min\": 37.0, \"max\": 54.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 60, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 48\n - Van Westendorp index: 54\n - Gabor-Granger rating: 46\n - Choice-based conjoint score: 37\n - Expert panel assessment: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.8, \"count\": 5, \"min\": 37.0, \"max\": 54.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 60, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 48\n - Van Westendorp index: 54\n - Gabor-Granger rating: 46\n - Choice-based conjoint score: 37\n - Expert panel assessment: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.8, \"count\": 5, \"min\": 37.0, \"max\": 54.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 60, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 38\n - Customer segment B survey score: 41\n - Focus group rating: 29\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.6, \"count\": 5, \"min\": 29.0, \"max\": 41.0, \"std\": 4.84}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 38\n - Customer segment B survey score: 41\n - Focus group rating: 29\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.6, \"count\": 5, \"min\": 29.0, \"max\": 41.0, \"std\": 4.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 38\n - Customer segment B survey score: 41\n - Focus group rating: 29\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.6, \"count\": 5, \"min\": 29.0, \"max\": 41.0, \"std\": 4.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 38\n - Customer segment B survey score: 41\n - Focus group rating: 29\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.6, \"count\": 5, \"min\": 29.0, \"max\": 41.0, \"std\": 4.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 38\n - Customer segment B survey score: 41\n - Focus group rating: 29\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.6, \"count\": 5, \"min\": 29.0, \"max\": 41.0, \"std\": 4.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 69\n - In-store intercept rating: 63\n - Trade-off analysis index: 76\n - Retention predictor score: 68\n - Benchmark comparison rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 63.0, \"max\": 76.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 69\n - In-store intercept rating: 63\n - Trade-off analysis index: 76\n - Retention predictor score: 68\n - Benchmark comparison rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 63.0, \"max\": 76.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 69\n - In-store intercept rating: 63\n - Trade-off analysis index: 76\n - Retention predictor score: 68\n - Benchmark comparison rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 63.0, \"max\": 76.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 69\n - In-store intercept rating: 63\n - Trade-off analysis index: 76\n - Retention predictor score: 68\n - Benchmark comparison rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 63.0, \"max\": 76.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 69\n - In-store intercept rating: 63\n - Trade-off analysis index: 76\n - Retention predictor score: 68\n - Benchmark comparison rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 63.0, \"max\": 76.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 44\n - Van Westendorp index: 49\n - Gabor-Granger rating: 37\n - Choice-based conjoint score: 44\n - Expert panel assessment: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 37.0, \"max\": 52.0, \"std\": 5.11}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 44\n - Van Westendorp index: 49\n - Gabor-Granger rating: 37\n - Choice-based conjoint score: 44\n - Expert panel assessment: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 37.0, \"max\": 52.0, \"std\": 5.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 44\n - Van Westendorp index: 49\n - Gabor-Granger rating: 37\n - Choice-based conjoint score: 44\n - Expert panel assessment: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 37.0, \"max\": 52.0, \"std\": 5.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 44\n - Van Westendorp index: 49\n - Gabor-Granger rating: 37\n - Choice-based conjoint score: 44\n - Expert panel assessment: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 37.0, \"max\": 52.0, \"std\": 5.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 44\n - Van Westendorp index: 49\n - Gabor-Granger rating: 37\n - Choice-based conjoint score: 44\n - Expert panel assessment: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 37.0, \"max\": 52.0, \"std\": 5.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 64\n - Customer segment B survey score: 57\n - Focus group rating: 52\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 3.85}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 37, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 64\n - Customer segment B survey score: 57\n - Focus group rating: 52\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 3.85}\n\nTool: check_external_reference\nOutput: {\"request_id\": 37, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 87, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 64\n - Customer segment B survey score: 57\n - Focus group rating: 52\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 3.85}\n\nTool: check_external_reference\nOutput: {\"request_id\": 87, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 37, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 64\n - Customer segment B survey score: 57\n - Focus group rating: 52\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 3.85}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 37, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 87, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 64\n - Customer segment B survey score: 57\n - Focus group rating: 52\n - Conjoint analysis index: 59\n - Historical price-sensitivity score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 3.85}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 87, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: 62\n - In-store intercept rating: 55\n - Trade-off analysis index: 45\n - Retention predictor score: 50\n - Benchmark comparison rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 42.0, \"max\": 62.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: 62\n - In-store intercept rating: 55\n - Trade-off analysis index: 45\n - Retention predictor score: 50\n - Benchmark comparison rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 42.0, \"max\": 62.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: 62\n - In-store intercept rating: 55\n - Trade-off analysis index: 45\n - Retention predictor score: 50\n - Benchmark comparison rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 42.0, \"max\": 62.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: 62\n - In-store intercept rating: 55\n - Trade-off analysis index: 45\n - Retention predictor score: 50\n - Benchmark comparison rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 42.0, \"max\": 62.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: 62\n - In-store intercept rating: 55\n - Trade-off analysis index: 45\n - Retention predictor score: 50\n - Benchmark comparison rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 42.0, \"max\": 62.0, \"std\": 7.14}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 49\n - Gabor-Granger rating: 51\n - Choice-based conjoint score: 47\n - Expert panel assessment: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.92}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 49\n - Gabor-Granger rating: 51\n - Choice-based conjoint score: 47\n - Expert panel assessment: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 49\n - Gabor-Granger rating: 51\n - Choice-based conjoint score: 47\n - Expert panel assessment: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 49\n - Gabor-Granger rating: 51\n - Choice-based conjoint score: 47\n - Expert panel assessment: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 49\n - Gabor-Granger rating: 51\n - Choice-based conjoint score: 47\n - Expert panel assessment: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: 42\n - Customer segment B survey score: 62\n - Focus group rating: 64\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 42.0, \"max\": 64.0, \"std\": 8.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: 42\n - Customer segment B survey score: 62\n - Focus group rating: 64\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 42.0, \"max\": 64.0, \"std\": 8.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 76, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: 42\n - Customer segment B survey score: 62\n - Focus group rating: 64\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 42.0, \"max\": 64.0, \"std\": 8.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 76, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: 42\n - Customer segment B survey score: 62\n - Focus group rating: 64\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 42.0, \"max\": 64.0, \"std\": 8.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 76, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: 42\n - Customer segment B survey score: 62\n - Focus group rating: 64\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 42.0, \"max\": 64.0, \"std\": 8.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 76, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 43\n - Trade-off analysis index: 45\n - Retention predictor score: 37\n - Benchmark comparison rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 33.0, \"max\": 58.0, \"std\": 8.54}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 43\n - Trade-off analysis index: 45\n - Retention predictor score: 37\n - Benchmark comparison rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 33.0, \"max\": 58.0, \"std\": 8.54}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 43\n - Trade-off analysis index: 45\n - Retention predictor score: 37\n - Benchmark comparison rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 33.0, \"max\": 58.0, \"std\": 8.54}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 43\n - Trade-off analysis index: 45\n - Retention predictor score: 37\n - Benchmark comparison rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 33.0, \"max\": 58.0, \"std\": 8.54}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 43\n - Trade-off analysis index: 45\n - Retention predictor score: 37\n - Benchmark comparison rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 33.0, \"max\": 58.0, \"std\": 8.54}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: 68\n - Van Westendorp index: 65\n - Gabor-Granger rating: 67\n - Choice-based conjoint score: 69\n - Expert panel assessment: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 7.03}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 41, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: 68\n - Van Westendorp index: 65\n - Gabor-Granger rating: 67\n - Choice-based conjoint score: 69\n - Expert panel assessment: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 7.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 41, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 91, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: 68\n - Van Westendorp index: 65\n - Gabor-Granger rating: 67\n - Choice-based conjoint score: 69\n - Expert panel assessment: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 7.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 91, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 41, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: 68\n - Van Westendorp index: 65\n - Gabor-Granger rating: 67\n - Choice-based conjoint score: 69\n - Expert panel assessment: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 7.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 41, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 91, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: 68\n - Van Westendorp index: 65\n - Gabor-Granger rating: 67\n - Choice-based conjoint score: 69\n - Expert panel assessment: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 7.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 91, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 29\n - Focus group rating: 53\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.2, \"count\": 5, \"min\": 29.0, \"max\": 55.0, \"std\": 10.32}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 29\n - Focus group rating: 53\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.2, \"count\": 5, \"min\": 29.0, \"max\": 55.0, \"std\": 10.32}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 29\n - Focus group rating: 53\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.2, \"count\": 5, \"min\": 29.0, \"max\": 55.0, \"std\": 10.32}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 29\n - Focus group rating: 53\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.2, \"count\": 5, \"min\": 29.0, \"max\": 55.0, \"std\": 10.32}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 29\n - Focus group rating: 53\n - Conjoint analysis index: 49\n - Historical price-sensitivity score: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.2, \"count\": 5, \"min\": 29.0, \"max\": 55.0, \"std\": 10.32}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 58\n - Trade-off analysis index: 59\n - Retention predictor score: 57\n - Benchmark comparison rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.8, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.66}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 58\n - Trade-off analysis index: 59\n - Retention predictor score: 57\n - Benchmark comparison rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.8, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 34, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 58\n - Trade-off analysis index: 59\n - Retention predictor score: 57\n - Benchmark comparison rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.8, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 58\n - Trade-off analysis index: 59\n - Retention predictor score: 57\n - Benchmark comparison rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.8, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 34, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: 58\n - In-store intercept rating: 58\n - Trade-off analysis index: 59\n - Retention predictor score: 57\n - Benchmark comparison rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.8, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 21\n - Van Westendorp index: 54\n - Gabor-Granger rating: 27\n - Choice-based conjoint score: 25\n - Expert panel assessment: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.2, \"count\": 5, \"min\": 21.0, \"max\": 54.0, \"std\": 12.01}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 5, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 21\n - Van Westendorp index: 54\n - Gabor-Granger rating: 27\n - Choice-based conjoint score: 25\n - Expert panel assessment: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.2, \"count\": 5, \"min\": 21.0, \"max\": 54.0, \"std\": 12.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 5, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 55, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 21\n - Van Westendorp index: 54\n - Gabor-Granger rating: 27\n - Choice-based conjoint score: 25\n - Expert panel assessment: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.2, \"count\": 5, \"min\": 21.0, \"max\": 54.0, \"std\": 12.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 5, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 21\n - Van Westendorp index: 54\n - Gabor-Granger rating: 27\n - Choice-based conjoint score: 25\n - Expert panel assessment: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.2, \"count\": 5, \"min\": 21.0, \"max\": 54.0, \"std\": 12.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 5, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 55, "offset": 25, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 21\n - Van Westendorp index: 54\n - Gabor-Granger rating: 27\n - Choice-based conjoint score: 25\n - Expert panel assessment: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.2, \"count\": 5, \"min\": 21.0, \"max\": 54.0, \"std\": 12.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: 30\n - Customer segment B survey score: 23\n - Focus group rating: 35\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.4, \"count\": 5, \"min\": 19.0, \"max\": 35.0, \"std\": 5.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 27, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: 30\n - Customer segment B survey score: 23\n - Focus group rating: 35\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.4, \"count\": 5, \"min\": 19.0, \"max\": 35.0, \"std\": 5.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 27, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 73, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: 30\n - Customer segment B survey score: 23\n - Focus group rating: 35\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.4, \"count\": 5, \"min\": 19.0, \"max\": 35.0, \"std\": 5.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 73, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 27, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: 30\n - Customer segment B survey score: 23\n - Focus group rating: 35\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.4, \"count\": 5, \"min\": 19.0, \"max\": 35.0, \"std\": 5.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 27, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 73, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: 30\n - Customer segment B survey score: 23\n - Focus group rating: 35\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.4, \"count\": 5, \"min\": 19.0, \"max\": 35.0, \"std\": 5.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 73, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 27, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 63\n - Trade-off analysis index: 70\n - Retention predictor score: 50\n - Benchmark comparison rating: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.2, \"count\": 5, \"min\": 50.0, \"max\": 71.0, \"std\": 7.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 63\n - Trade-off analysis index: 70\n - Retention predictor score: 50\n - Benchmark comparison rating: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.2, \"count\": 5, \"min\": 50.0, \"max\": 71.0, \"std\": 7.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 63\n - Trade-off analysis index: 70\n - Retention predictor score: 50\n - Benchmark comparison rating: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.2, \"count\": 5, \"min\": 50.0, \"max\": 71.0, \"std\": 7.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 63\n - Trade-off analysis index: 70\n - Retention predictor score: 50\n - Benchmark comparison rating: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.2, \"count\": 5, \"min\": 50.0, \"max\": 71.0, \"std\": 7.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 63\n - Trade-off analysis index: 70\n - Retention predictor score: 50\n - Benchmark comparison rating: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.2, \"count\": 5, \"min\": 50.0, \"max\": 71.0, \"std\": 7.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: 81\n - Van Westendorp index: 66\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 61\n - Expert panel assessment: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.0, \"count\": 5, \"min\": 57.0, \"max\": 81.0, \"std\": 8.27}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: 81\n - Van Westendorp index: 66\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 61\n - Expert panel assessment: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.0, \"count\": 5, \"min\": 57.0, \"max\": 81.0, \"std\": 8.27}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: 81\n - Van Westendorp index: 66\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 61\n - Expert panel assessment: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.0, \"count\": 5, \"min\": 57.0, \"max\": 81.0, \"std\": 8.27}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: 81\n - Van Westendorp index: 66\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 61\n - Expert panel assessment: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.0, \"count\": 5, \"min\": 57.0, \"max\": 81.0, \"std\": 8.27}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: 81\n - Van Westendorp index: 66\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 61\n - Expert panel assessment: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.0, \"count\": 5, \"min\": 57.0, \"max\": 81.0, \"std\": 8.27}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 28\n - Focus group rating: 24\n - Conjoint analysis index: 56\n - Historical price-sensitivity score: 24\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.4, \"count\": 5, \"min\": 24.0, \"max\": 56.0, \"std\": 11.99}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 28\n - Focus group rating: 24\n - Conjoint analysis index: 56\n - Historical price-sensitivity score: 24\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.4, \"count\": 5, \"min\": 24.0, \"max\": 56.0, \"std\": 11.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 28\n - Focus group rating: 24\n - Conjoint analysis index: 56\n - Historical price-sensitivity score: 24\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.4, \"count\": 5, \"min\": 24.0, \"max\": 56.0, \"std\": 11.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 28\n - Focus group rating: 24\n - Conjoint analysis index: 56\n - Historical price-sensitivity score: 24\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.4, \"count\": 5, \"min\": 24.0, \"max\": 56.0, \"std\": 11.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 35\n - Customer segment B survey score: 28\n - Focus group rating: 24\n - Conjoint analysis index: 56\n - Historical price-sensitivity score: 24\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.4, \"count\": 5, \"min\": 24.0, \"max\": 56.0, \"std\": 11.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: 68\n - Trade-off analysis index: 62\n - Retention predictor score: 63\n - Benchmark comparison rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 37.0, \"max\": 68.0, \"std\": 10.83}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: 68\n - Trade-off analysis index: 62\n - Retention predictor score: 63\n - Benchmark comparison rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 37.0, \"max\": 68.0, \"std\": 10.83}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 99, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: 68\n - Trade-off analysis index: 62\n - Retention predictor score: 63\n - Benchmark comparison rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 37.0, \"max\": 68.0, \"std\": 10.83}\n\nTool: check_external_reference\nOutput: {\"request_id\": 99, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: 68\n - Trade-off analysis index: 62\n - Retention predictor score: 63\n - Benchmark comparison rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 37.0, \"max\": 68.0, \"std\": 10.83}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 99, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: 68\n - Trade-off analysis index: 62\n - Retention predictor score: 63\n - Benchmark comparison rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 5, \"min\": 37.0, \"max\": 68.0, \"std\": 10.83}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 99, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 56\n - Van Westendorp index: 43\n - Gabor-Granger rating: 53\n - Choice-based conjoint score: 50\n - Expert panel assessment: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 64.0, \"std\": 6.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 12, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 56\n - Van Westendorp index: 43\n - Gabor-Granger rating: 53\n - Choice-based conjoint score: 50\n - Expert panel assessment: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 64.0, \"std\": 6.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 12, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 92, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 56\n - Van Westendorp index: 43\n - Gabor-Granger rating: 53\n - Choice-based conjoint score: 50\n - Expert panel assessment: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 64.0, \"std\": 6.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 92, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 12, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 56\n - Van Westendorp index: 43\n - Gabor-Granger rating: 53\n - Choice-based conjoint score: 50\n - Expert panel assessment: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 64.0, \"std\": 6.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 12, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 92, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 56\n - Van Westendorp index: 43\n - Gabor-Granger rating: 53\n - Choice-based conjoint score: 50\n - Expert panel assessment: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 64.0, \"std\": 6.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 92, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 71\n - Customer segment B survey score: 48\n - Focus group rating: 63\n - Conjoint analysis index: 53\n - Historical price-sensitivity score: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.62}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 71\n - Customer segment B survey score: 48\n - Focus group rating: 63\n - Conjoint analysis index: 53\n - Historical price-sensitivity score: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 71\n - Customer segment B survey score: 48\n - Focus group rating: 63\n - Conjoint analysis index: 53\n - Historical price-sensitivity score: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 71\n - Customer segment B survey score: 48\n - Focus group rating: 63\n - Conjoint analysis index: 53\n - Historical price-sensitivity score: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 71\n - Customer segment B survey score: 48\n - Focus group rating: 63\n - Conjoint analysis index: 53\n - Historical price-sensitivity score: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 71\n - Trade-off analysis index: 72\n - Retention predictor score: 63\n - Benchmark comparison rating: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.6, \"count\": 5, \"min\": 63.0, \"max\": 75.0, \"std\": 4.18}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 71\n - Trade-off analysis index: 72\n - Retention predictor score: 63\n - Benchmark comparison rating: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.6, \"count\": 5, \"min\": 63.0, \"max\": 75.0, \"std\": 4.18}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 71\n - Trade-off analysis index: 72\n - Retention predictor score: 63\n - Benchmark comparison rating: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.6, \"count\": 5, \"min\": 63.0, \"max\": 75.0, \"std\": 4.18}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 71\n - Trade-off analysis index: 72\n - Retention predictor score: 63\n - Benchmark comparison rating: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.6, \"count\": 5, \"min\": 63.0, \"max\": 75.0, \"std\": 4.18}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 67\n - In-store intercept rating: 71\n - Trade-off analysis index: 72\n - Retention predictor score: 63\n - Benchmark comparison rating: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.6, \"count\": 5, \"min\": 63.0, \"max\": 75.0, \"std\": 4.18}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 59\n - Van Westendorp index: 76\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 67\n - Expert panel assessment: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.4, \"count\": 5, \"min\": 45.0, \"max\": 76.0, \"std\": 10.71}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 59\n - Van Westendorp index: 76\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 67\n - Expert panel assessment: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.4, \"count\": 5, \"min\": 45.0, \"max\": 76.0, \"std\": 10.71}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 59\n - Van Westendorp index: 76\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 67\n - Expert panel assessment: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.4, \"count\": 5, \"min\": 45.0, \"max\": 76.0, \"std\": 10.71}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 59\n - Van Westendorp index: 76\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 67\n - Expert panel assessment: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.4, \"count\": 5, \"min\": 45.0, \"max\": 76.0, \"std\": 10.71}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 59\n - Van Westendorp index: 76\n - Gabor-Granger rating: 70\n - Choice-based conjoint score: 67\n - Expert panel assessment: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.4, \"count\": 5, \"min\": 45.0, \"max\": 76.0, \"std\": 10.71}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 51\n - Customer segment B survey score: 43\n - Focus group rating: 36\n - Conjoint analysis index: 34\n - Historical price-sensitivity score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.4, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 51\n - Customer segment B survey score: 43\n - Focus group rating: 36\n - Conjoint analysis index: 34\n - Historical price-sensitivity score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.4, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 51\n - Customer segment B survey score: 43\n - Focus group rating: 36\n - Conjoint analysis index: 34\n - Historical price-sensitivity score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.4, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 51\n - Customer segment B survey score: 43\n - Focus group rating: 36\n - Conjoint analysis index: 34\n - Historical price-sensitivity score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.4, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-e-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 40, "difficulty": "easy", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 51\n - Customer segment B survey score: 43\n - Focus group rating: 36\n - Conjoint analysis index: 34\n - Historical price-sensitivity score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.4, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: 67\n - Trade-off analysis index: [data not available]\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 47.0, \"max\": 67.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 41, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: 67\n - Trade-off analysis index: [data not available]\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 47.0, \"max\": 67.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 41, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 71, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: 67\n - Trade-off analysis index: [data not available]\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 47.0, \"max\": 67.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 71, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 41, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: 67\n - Trade-off analysis index: [data not available]\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 47.0, \"max\": 67.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 41, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 71, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: 67\n - Trade-off analysis index: [data not available]\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 47.0, \"max\": 67.0, \"std\": 8.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 71, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 16\n - Van Westendorp index: 45\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 11\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.0, \"count\": 3, \"min\": 11.0, \"max\": 45.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 24, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 16\n - Van Westendorp index: 45\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 11\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.0, \"count\": 3, \"min\": 11.0, \"max\": 45.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 24, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 16\n - Van Westendorp index: 45\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 11\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.0, \"count\": 3, \"min\": 11.0, \"max\": 45.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 48, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 24, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 16\n - Van Westendorp index: 45\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 11\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.0, \"count\": 3, \"min\": 11.0, \"max\": 45.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 24, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: 16\n - Van Westendorp index: 45\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 11\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.0, \"count\": 3, \"min\": 11.0, \"max\": 45.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 48, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 24, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 75\n - Focus group rating: 41\n - Conjoint analysis index: 55\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 41.0, \"max\": 75.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 75\n - Focus group rating: 41\n - Conjoint analysis index: 55\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 41.0, \"max\": 75.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 56, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 75\n - Focus group rating: 41\n - Conjoint analysis index: 55\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 41.0, \"max\": 75.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"request_id\": 56, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 75\n - Focus group rating: 41\n - Conjoint analysis index: 55\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 41.0, \"max\": 75.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 56, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 75\n - Focus group rating: 41\n - Conjoint analysis index: 55\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 41.0, \"max\": 75.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 56, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 40\n - In-store intercept rating: 67\n - Trade-off analysis index: 56\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.33, \"count\": 3, \"min\": 40.0, \"max\": 67.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 39, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 40\n - In-store intercept rating: 67\n - Trade-off analysis index: 56\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.33, \"count\": 3, \"min\": 40.0, \"max\": 67.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 39, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 40\n - In-store intercept rating: 67\n - Trade-off analysis index: 56\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.33, \"count\": 3, \"min\": 40.0, \"max\": 67.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 39, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 40\n - In-store intercept rating: 67\n - Trade-off analysis index: 56\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.33, \"count\": 3, \"min\": 40.0, \"max\": 67.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 39, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 40\n - In-store intercept rating: 67\n - Trade-off analysis index: 56\n - Retention predictor score: [data not available]\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.33, \"count\": 3, \"min\": 40.0, \"max\": 67.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 7\n - Gabor-Granger rating: 34\n - Choice-based conjoint score: 70\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 7.0, \"max\": 70.0, \"std\": 25.81}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 7\n - Gabor-Granger rating: 34\n - Choice-based conjoint score: 70\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 7.0, \"max\": 70.0, \"std\": 25.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 7\n - Gabor-Granger rating: 34\n - Choice-based conjoint score: 70\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 7.0, \"max\": 70.0, \"std\": 25.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 7\n - Gabor-Granger rating: 34\n - Choice-based conjoint score: 70\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 7.0, \"max\": 70.0, \"std\": 25.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 7\n - Gabor-Granger rating: 34\n - Choice-based conjoint score: 70\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 7.0, \"max\": 70.0, \"std\": 25.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 78\n - Focus group rating: [data not available]\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 18\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 18.0, \"max\": 78.0, \"std\": 26.08}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 78\n - Focus group rating: [data not available]\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 18\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 18.0, \"max\": 78.0, \"std\": 26.08}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 45, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 78\n - Focus group rating: [data not available]\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 18\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 18.0, \"max\": 78.0, \"std\": 26.08}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 78\n - Focus group rating: [data not available]\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 18\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 18.0, \"max\": 78.0, \"std\": 26.08}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 45, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 78\n - Focus group rating: [data not available]\n - Conjoint analysis index: 29\n - Historical price-sensitivity score: 18\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 18.0, \"max\": 78.0, \"std\": 26.08}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 71\n - Trade-off analysis index: 65\n - Retention predictor score: 67\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 65.0, \"max\": 71.0, \"std\": 2.49}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 42, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 71\n - Trade-off analysis index: 65\n - Retention predictor score: 67\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 65.0, \"max\": 71.0, \"std\": 2.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 42, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 71\n - Trade-off analysis index: 65\n - Retention predictor score: 67\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 65.0, \"max\": 71.0, \"std\": 2.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 42, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 71\n - Trade-off analysis index: 65\n - Retention predictor score: 67\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 65.0, \"max\": 71.0, \"std\": 2.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 42, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 71\n - Trade-off analysis index: 65\n - Retention predictor score: 67\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 65.0, \"max\": 71.0, \"std\": 2.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 53\n - Gabor-Granger rating: 28\n - Choice-based conjoint score: 59\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 59.0, \"std\": 13.42}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 53\n - Gabor-Granger rating: 28\n - Choice-based conjoint score: 59\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 59.0, \"std\": 13.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 53\n - Gabor-Granger rating: 28\n - Choice-based conjoint score: 59\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 59.0, \"std\": 13.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 53\n - Gabor-Granger rating: 28\n - Choice-based conjoint score: 59\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 59.0, \"std\": 13.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 53\n - Gabor-Granger rating: 28\n - Choice-based conjoint score: 59\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 59.0, \"std\": 13.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 60\n - Focus group rating: 36\n - Conjoint analysis index: 28\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.33, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 60\n - Focus group rating: 36\n - Conjoint analysis index: 28\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.33, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 49, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 60\n - Focus group rating: 36\n - Conjoint analysis index: 28\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.33, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 49, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 60\n - Focus group rating: 36\n - Conjoint analysis index: 28\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.33, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 49, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 60\n - Focus group rating: 36\n - Conjoint analysis index: 28\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.33, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 49, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 34\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 54\n - Retention predictor score: 81\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 34.0, \"max\": 81.0, \"std\": 19.26}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 34\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 54\n - Retention predictor score: 81\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 34.0, \"max\": 81.0, \"std\": 19.26}\n\nTool: check_external_reference\nOutput: {\"request_id\": 38, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 34\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 54\n - Retention predictor score: 81\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 34.0, \"max\": 81.0, \"std\": 19.26}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 34\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 54\n - Retention predictor score: 81\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 34.0, \"max\": 81.0, \"std\": 19.26}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 38, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Online panel score: 34\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 54\n - Retention predictor score: 81\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 34.0, \"max\": 81.0, \"std\": 19.26}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 72\n - Gabor-Granger rating: 45\n - Choice-based conjoint score: 37\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 37.0, \"max\": 72.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 72\n - Gabor-Granger rating: 45\n - Choice-based conjoint score: 37\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 37.0, \"max\": 72.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 63, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 72\n - Gabor-Granger rating: 45\n - Choice-based conjoint score: 37\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 37.0, \"max\": 72.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 63, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 72\n - Gabor-Granger rating: 45\n - Choice-based conjoint score: 37\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 37.0, \"max\": 72.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 63, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 72\n - Gabor-Granger rating: 45\n - Choice-based conjoint score: 37\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 37.0, \"max\": 72.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 63, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 45\n - Focus group rating: [data not available]\n - Conjoint analysis index: 41\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 41.0, \"max\": 56.0, \"std\": 6.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 14, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 45\n - Focus group rating: [data not available]\n - Conjoint analysis index: 41\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 41.0, \"max\": 56.0, \"std\": 6.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 14, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 64, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 45\n - Focus group rating: [data not available]\n - Conjoint analysis index: 41\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 41.0, \"max\": 56.0, \"std\": 6.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 64, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 14, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 45\n - Focus group rating: [data not available]\n - Conjoint analysis index: 41\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 41.0, \"max\": 56.0, \"std\": 6.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 14, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 64, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 45\n - Focus group rating: [data not available]\n - Conjoint analysis index: 41\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 41.0, \"max\": 56.0, \"std\": 6.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 64, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 69\n - Trade-off analysis index: 53\n - Retention predictor score: 22\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 22.0, \"max\": 69.0, \"std\": 19.51}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 69\n - Trade-off analysis index: 53\n - Retention predictor score: 22\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 22.0, \"max\": 69.0, \"std\": 19.51}\n\nTool: check_external_reference\nOutput: {\"request_id\": 34, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 69\n - Trade-off analysis index: 53\n - Retention predictor score: 22\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 22.0, \"max\": 69.0, \"std\": 19.51}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 69\n - Trade-off analysis index: 53\n - Retention predictor score: 22\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 22.0, \"max\": 69.0, \"std\": 19.51}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 34, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: 69\n - Trade-off analysis index: 53\n - Retention predictor score: 22\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 22.0, \"max\": 69.0, \"std\": 19.51}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 57\n - Van Westendorp index: [data not available]\n - Gabor-Granger rating: 54\n - Choice-based conjoint score: 22\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 22.0, \"max\": 57.0, \"std\": 15.84}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 37, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 57\n - Van Westendorp index: [data not available]\n - Gabor-Granger rating: 54\n - Choice-based conjoint score: 22\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 22.0, \"max\": 57.0, \"std\": 15.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 37, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 87, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 57\n - Van Westendorp index: [data not available]\n - Gabor-Granger rating: 54\n - Choice-based conjoint score: 22\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 22.0, \"max\": 57.0, \"std\": 15.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 87, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 37, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 57\n - Van Westendorp index: [data not available]\n - Gabor-Granger rating: 54\n - Choice-based conjoint score: 22\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 22.0, \"max\": 57.0, \"std\": 15.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 37, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 87, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Direct elicitation score: 57\n - Van Westendorp index: [data not available]\n - Gabor-Granger rating: 54\n - Choice-based conjoint score: 22\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 22.0, \"max\": 57.0, \"std\": 15.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 87, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 35\n - Focus group rating: 63\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 35.0, \"max\": 63.0, \"std\": 11.9}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 35\n - Focus group rating: 63\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 35.0, \"max\": 63.0, \"std\": 11.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 35\n - Focus group rating: 63\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 35.0, \"max\": 63.0, \"std\": 11.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 35\n - Focus group rating: 63\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 35.0, \"max\": 63.0, \"std\": 11.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Customer segment A survey score: [data not available]\n - Customer segment B survey score: 35\n - Focus group rating: 63\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 35.0, \"max\": 63.0, \"std\": 11.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 58\n - Benchmark comparison rating: 20\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 20.0, \"max\": 58.0, \"std\": 15.54}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 11, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 58\n - Benchmark comparison rating: 20\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 20.0, \"max\": 58.0, \"std\": 15.54}\n\nTool: check_external_reference\nOutput: {\"request_id\": 11, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 61, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 58\n - Benchmark comparison rating: 20\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 20.0, \"max\": 58.0, \"std\": 15.54}\n\nTool: check_external_reference\nOutput: {\"request_id\": 61, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 11, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 58\n - Benchmark comparison rating: 20\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 20.0, \"max\": 58.0, \"std\": 15.54}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 11, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 61, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Online panel score: 37\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 58\n - Benchmark comparison rating: 20\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 20.0, \"max\": 58.0, \"std\": 15.54}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 61, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 42\n - Choice-based conjoint score: 35\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.67, \"count\": 3, \"min\": 35.0, \"max\": 60.0, \"std\": 10.53}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 9, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 42\n - Choice-based conjoint score: 35\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.67, \"count\": 3, \"min\": 35.0, \"max\": 60.0, \"std\": 10.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 9, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 42\n - Choice-based conjoint score: 35\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.67, \"count\": 3, \"min\": 35.0, \"max\": 60.0, \"std\": 10.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 9, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 42\n - Choice-based conjoint score: 35\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.67, \"count\": 3, \"min\": 35.0, \"max\": 60.0, \"std\": 10.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 9, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 42\n - Choice-based conjoint score: 35\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.67, \"count\": 3, \"min\": 35.0, \"max\": 60.0, \"std\": 10.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 40\n - Customer segment B survey score: [data not available]\n - Focus group rating: 58\n - Conjoint analysis index: 17\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 17.0, \"max\": 58.0, \"std\": 16.78}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 40\n - Customer segment B survey score: [data not available]\n - Focus group rating: 58\n - Conjoint analysis index: 17\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 17.0, \"max\": 58.0, \"std\": 16.78}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 40\n - Customer segment B survey score: [data not available]\n - Focus group rating: 58\n - Conjoint analysis index: 17\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 17.0, \"max\": 58.0, \"std\": 16.78}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 40\n - Customer segment B survey score: [data not available]\n - Focus group rating: 58\n - Conjoint analysis index: 17\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 17.0, \"max\": 58.0, \"std\": 16.78}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Customer segment A survey score: 40\n - Customer segment B survey score: [data not available]\n - Focus group rating: 58\n - Conjoint analysis index: 17\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.33, \"count\": 3, \"min\": 17.0, \"max\": 58.0, \"std\": 16.78}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 39\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 30\n - Retention predictor score: 40\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 30.0, \"max\": 40.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 39\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 30\n - Retention predictor score: 40\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 30.0, \"max\": 40.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 39\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 30\n - Retention predictor score: 40\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 30.0, \"max\": 40.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 39\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 30\n - Retention predictor score: 40\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 30.0, \"max\": 40.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Online panel score: 39\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 30\n - Retention predictor score: 40\n - Benchmark comparison rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 30.0, \"max\": 40.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 61\n - Van Westendorp index: 36\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 36.0, \"max\": 61.0, \"std\": 11.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 61\n - Van Westendorp index: 36\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 36.0, \"max\": 61.0, \"std\": 11.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 61\n - Van Westendorp index: 36\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 36.0, \"max\": 61.0, \"std\": 11.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 61\n - Van Westendorp index: 36\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 36.0, \"max\": 61.0, \"std\": 11.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 25, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Direct elicitation score: 61\n - Van Westendorp index: 36\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 36.0, \"max\": 61.0, \"std\": 11.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 56\n - Customer segment B survey score: 74\n - Focus group rating: [data not available]\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 19.0, \"max\": 74.0, \"std\": 22.9}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 56\n - Customer segment B survey score: 74\n - Focus group rating: [data not available]\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 19.0, \"max\": 74.0, \"std\": 22.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 56\n - Customer segment B survey score: 74\n - Focus group rating: [data not available]\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 19.0, \"max\": 74.0, \"std\": 22.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 56\n - Customer segment B survey score: 74\n - Focus group rating: [data not available]\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 19.0, \"max\": 74.0, \"std\": 22.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Customer segment A survey score: 56\n - Customer segment B survey score: 74\n - Focus group rating: [data not available]\n - Conjoint analysis index: 19\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 19.0, \"max\": 74.0, \"std\": 22.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 73\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 47.0, \"max\": 73.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 6, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 73\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 47.0, \"max\": 73.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 6, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 86, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 73\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 47.0, \"max\": 73.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 86, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 6, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 73\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 47.0, \"max\": 73.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 6, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 86, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Online panel score: 47\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 73\n - Benchmark comparison rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 47.0, \"max\": 73.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 86, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 51\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 61\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.67, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.24}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 51\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 61\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.67, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.24}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 51\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 61\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.67, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.24}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 51\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 61\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.67, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.24}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA B2B software vendor is evaluating enterprise pricing sensitivity. The following survey scores were collected.\n\nEvidence:\n - Direct elicitation score: 46\n - Van Westendorp index: 51\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: 61\n - Expert panel assessment: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.67, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.24}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 16\n - Customer segment B survey score: 68\n - Focus group rating: 43\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 16.0, \"max\": 68.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 16\n - Customer segment B survey score: 68\n - Focus group rating: 43\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 16.0, \"max\": 68.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 16\n - Customer segment B survey score: 68\n - Focus group rating: 43\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 16.0, \"max\": 68.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 16\n - Customer segment B survey score: 68\n - Focus group rating: 43\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 16.0, \"max\": 68.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA streaming service is assessing subscriber tolerance for a price increase. Five research instruments produced the following results.\n\nEvidence:\n - Customer segment A survey score: 16\n - Customer segment B survey score: 68\n - Focus group rating: 43\n - Conjoint analysis index: [data not available]\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 16.0, \"max\": 68.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 38\n - Retention predictor score: 27\n - Benchmark comparison rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 27.0, \"max\": 61.0, \"std\": 14.17}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 38\n - Retention predictor score: 27\n - Benchmark comparison rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 27.0, \"max\": 61.0, \"std\": 14.17}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 38\n - Retention predictor score: 27\n - Benchmark comparison rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 27.0, \"max\": 61.0, \"std\": 14.17}\n\nTool: check_external_reference\nOutput: {\"request_id\": 88, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 38\n - Retention predictor score: 27\n - Benchmark comparison rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 27.0, \"max\": 61.0, \"std\": 14.17}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA specialty food brand is testing premium pricing for an organic product line. Market research scores are summarized below.\n\nEvidence:\n - Online panel score: [data not available]\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: 38\n - Retention predictor score: 27\n - Benchmark comparison rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 27.0, \"max\": 61.0, \"std\": 14.17}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 88, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 55\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 55.0, \"max\": 62.0, \"std\": 2.94}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 55\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 55.0, \"max\": 62.0, \"std\": 2.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 97, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 55\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 55.0, \"max\": 62.0, \"std\": 2.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 97, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 55\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 55.0, \"max\": 62.0, \"std\": 2.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 97, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA product team is evaluating customer willingness-to-pay for a new subscription tier. Five independent market signals have been collected.\n\nEvidence:\n - Direct elicitation score: [data not available]\n - Van Westendorp index: 60\n - Gabor-Granger rating: 55\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 55.0, \"max\": 62.0, \"std\": 2.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 97, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 45\n - Customer segment B survey score: [data not available]\n - Focus group rating: 66\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 45.0, \"max\": 69.0, \"std\": 10.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 9, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 45\n - Customer segment B survey score: [data not available]\n - Focus group rating: 66\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 45.0, \"max\": 69.0, \"std\": 10.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 9, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 89, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 45\n - Customer segment B survey score: [data not available]\n - Focus group rating: 66\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 45.0, \"max\": 69.0, \"std\": 10.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 89, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 9, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 45\n - Customer segment B survey score: [data not available]\n - Focus group rating: 66\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 45.0, \"max\": 69.0, \"std\": 10.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 9, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 89, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA SaaS company is pricing a premium add-on. The following customer research scores (0–100) indicate price tolerance.\n\nEvidence:\n - Customer segment A survey score: 45\n - Customer segment B survey score: [data not available]\n - Focus group rating: 66\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 45.0, \"max\": 69.0, \"std\": 10.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 89, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these signals, what is your best estimate for the WTP index on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 72\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 25\n - Benchmark comparison rating: 94\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 25.0, \"max\": 94.0, \"std\": 28.78}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 72\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 25\n - Benchmark comparison rating: 94\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 25.0, \"max\": 94.0, \"std\": 28.78}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 72\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 25\n - Benchmark comparison rating: 94\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 25.0, \"max\": 94.0, \"std\": 28.78}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 72\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 25\n - Benchmark comparison rating: 94\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 25.0, \"max\": 94.0, \"std\": 28.78}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA retail brand is testing a new price point. Survey data from five customer panels is summarized below.\n\nEvidence:\n - Online panel score: 72\n - In-store intercept rating: [data not available]\n - Trade-off analysis index: [data not available]\n - Retention predictor score: 25\n - Benchmark comparison rating: 94\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 25.0, \"max\": 94.0, \"std\": 28.78}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall willingness-to-pay index (0–100) do you estimate based on these data points?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 38\n - Van Westendorp index: 60\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 38.0, \"max\": 60.0, \"std\": 8.99}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 38\n - Van Westendorp index: 60\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 38.0, \"max\": 60.0, \"std\": 8.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 38\n - Van Westendorp index: 60\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 38.0, \"max\": 60.0, \"std\": 8.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 38\n - Van Westendorp index: 60\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 38.0, \"max\": 60.0, \"std\": 8.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn e-commerce platform is assessing willingness-to-pay for expedited shipping. Customer research produced the following indices.\n\nEvidence:\n - Direct elicitation score: 38\n - Van Westendorp index: 60\n - Gabor-Granger rating: [data not available]\n - Choice-based conjoint score: [data not available]\n - Expert panel assessment: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 38.0, \"max\": 60.0, \"std\": 8.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite WTP score (0–100) from the evidence provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 50\n - Customer segment B survey score: [data not available]\n - Focus group rating: 84\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 50.0, \"max\": 84.0, \"std\": 13.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 50\n - Customer segment B survey score: [data not available]\n - Focus group rating: 84\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 50.0, \"max\": 84.0, \"std\": 13.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 1, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 50\n - Customer segment B survey score: [data not available]\n - Focus group rating: 84\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 50.0, \"max\": 84.0, \"std\": 13.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 50\n - Customer segment B survey score: [data not available]\n - Focus group rating: 84\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 50.0, \"max\": 84.0, \"std\": 13.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 1, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-pricing_wtp-h-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "hard", "domain": "pricing_wtp", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA luxury hotel chain is determining pricing for a new loyalty program tier. Customer research indices are available.\n\nEvidence:\n - Customer segment A survey score: 50\n - Customer segment B survey score: [data not available]\n - Focus group rating: 84\n - Conjoint analysis index: 69\n - Historical price-sensitivity score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.67, \"count\": 3, \"min\": 50.0, \"max\": 84.0, \"std\": 13.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the evidence above, estimate the overall willingness-to-pay index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 42\n - Downtime frequency rating: 42\n - Output consistency score: 32\n - Continuous improvement rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.4, \"count\": 5, \"min\": 32.0, \"max\": 42.0, \"std\": 3.88}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 42\n - Downtime frequency rating: 42\n - Output consistency score: 32\n - Continuous improvement rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.4, \"count\": 5, \"min\": 32.0, \"max\": 42.0, \"std\": 3.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 42\n - Downtime frequency rating: 42\n - Output consistency score: 32\n - Continuous improvement rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.4, \"count\": 5, \"min\": 32.0, \"max\": 42.0, \"std\": 3.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 48, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 42\n - Downtime frequency rating: 42\n - Output consistency score: 32\n - Continuous improvement rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.4, \"count\": 5, \"min\": 32.0, \"max\": 42.0, \"std\": 3.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 42\n - Downtime frequency rating: 42\n - Output consistency score: 32\n - Continuous improvement rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.4, \"count\": 5, \"min\": 32.0, \"max\": 42.0, \"std\": 3.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 48, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 44\n - Labor efficiency index: 52\n - Inventory turnover rating: 33\n - Safety compliance score: 71\n - Energy per unit output rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.8, \"count\": 5, \"min\": 33.0, \"max\": 71.0, \"std\": 12.92}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 44\n - Labor efficiency index: 52\n - Inventory turnover rating: 33\n - Safety compliance score: 71\n - Energy per unit output rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.8, \"count\": 5, \"min\": 33.0, \"max\": 71.0, \"std\": 12.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 36, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 44\n - Labor efficiency index: 52\n - Inventory turnover rating: 33\n - Safety compliance score: 71\n - Energy per unit output rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.8, \"count\": 5, \"min\": 33.0, \"max\": 71.0, \"std\": 12.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 44\n - Labor efficiency index: 52\n - Inventory turnover rating: 33\n - Safety compliance score: 71\n - Energy per unit output rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.8, \"count\": 5, \"min\": 33.0, \"max\": 71.0, \"std\": 12.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 36, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 44\n - Labor efficiency index: 52\n - Inventory turnover rating: 33\n - Safety compliance score: 71\n - Energy per unit output rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.8, \"count\": 5, \"min\": 33.0, \"max\": 71.0, \"std\": 12.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 14\n - Team productivity index: 30\n - Cycle-time benchmark rating: 25\n - Quality-adjusted throughput score: 30\n - Lean assessment rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 14.0, \"max\": 40.0, \"std\": 8.45}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 14\n - Team productivity index: 30\n - Cycle-time benchmark rating: 25\n - Quality-adjusted throughput score: 30\n - Lean assessment rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 14.0, \"max\": 40.0, \"std\": 8.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 14\n - Team productivity index: 30\n - Cycle-time benchmark rating: 25\n - Quality-adjusted throughput score: 30\n - Lean assessment rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 14.0, \"max\": 40.0, \"std\": 8.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 14\n - Team productivity index: 30\n - Cycle-time benchmark rating: 25\n - Quality-adjusted throughput score: 30\n - Lean assessment rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 14.0, \"max\": 40.0, \"std\": 8.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 14\n - Team productivity index: 30\n - Cycle-time benchmark rating: 25\n - Quality-adjusted throughput score: 30\n - Lean assessment rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 14.0, \"max\": 40.0, \"std\": 8.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 50\n - Downtime frequency rating: 45\n - Output consistency score: 62\n - Continuous improvement rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 45.0, \"max\": 62.0, \"std\": 6.28}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 34, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 50\n - Downtime frequency rating: 45\n - Output consistency score: 62\n - Continuous improvement rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 45.0, \"max\": 62.0, \"std\": 6.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 34, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 64, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 50\n - Downtime frequency rating: 45\n - Output consistency score: 62\n - Continuous improvement rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 45.0, \"max\": 62.0, \"std\": 6.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 64, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 34, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 50\n - Downtime frequency rating: 45\n - Output consistency score: 62\n - Continuous improvement rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 45.0, \"max\": 62.0, \"std\": 6.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 34, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 64, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 50\n - Downtime frequency rating: 45\n - Output consistency score: 62\n - Continuous improvement rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 45.0, \"max\": 62.0, \"std\": 6.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 64, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 55\n - Labor efficiency index: 58\n - Inventory turnover rating: 43\n - Safety compliance score: 63\n - Energy per unit output rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 43.0, \"max\": 63.0, \"std\": 7.46}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 37, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 55\n - Labor efficiency index: 58\n - Inventory turnover rating: 43\n - Safety compliance score: 63\n - Energy per unit output rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 43.0, \"max\": 63.0, \"std\": 7.46}\n\nTool: check_external_reference\nOutput: {\"request_id\": 37, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 55\n - Labor efficiency index: 58\n - Inventory turnover rating: 43\n - Safety compliance score: 63\n - Energy per unit output rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 43.0, \"max\": 63.0, \"std\": 7.46}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 37, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 55\n - Labor efficiency index: 58\n - Inventory turnover rating: 43\n - Safety compliance score: 63\n - Energy per unit output rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 43.0, \"max\": 63.0, \"std\": 7.46}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 37, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 55\n - Labor efficiency index: 58\n - Inventory turnover rating: 43\n - Safety compliance score: 63\n - Energy per unit output rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 43.0, \"max\": 63.0, \"std\": 7.46}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 70\n - Team productivity index: 63\n - Cycle-time benchmark rating: 49\n - Quality-adjusted throughput score: 68\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 49.0, \"max\": 70.0, \"std\": 7.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 70\n - Team productivity index: 63\n - Cycle-time benchmark rating: 49\n - Quality-adjusted throughput score: 68\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 49.0, \"max\": 70.0, \"std\": 7.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 70\n - Team productivity index: 63\n - Cycle-time benchmark rating: 49\n - Quality-adjusted throughput score: 68\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 49.0, \"max\": 70.0, \"std\": 7.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 70\n - Team productivity index: 63\n - Cycle-time benchmark rating: 49\n - Quality-adjusted throughput score: 68\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 49.0, \"max\": 70.0, \"std\": 7.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 70\n - Team productivity index: 63\n - Cycle-time benchmark rating: 49\n - Quality-adjusted throughput score: 68\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 49.0, \"max\": 70.0, \"std\": 7.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 35\n - Capacity utilization index: 26\n - Downtime frequency rating: 36\n - Output consistency score: 30\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 26.0, \"max\": 41.0, \"std\": 5.16}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 35\n - Capacity utilization index: 26\n - Downtime frequency rating: 36\n - Output consistency score: 30\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 26.0, \"max\": 41.0, \"std\": 5.16}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 35\n - Capacity utilization index: 26\n - Downtime frequency rating: 36\n - Output consistency score: 30\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 26.0, \"max\": 41.0, \"std\": 5.16}\n\nTool: check_external_reference\nOutput: {\"request_id\": 48, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 35\n - Capacity utilization index: 26\n - Downtime frequency rating: 36\n - Output consistency score: 30\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 26.0, \"max\": 41.0, \"std\": 5.16}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 35\n - Capacity utilization index: 26\n - Downtime frequency rating: 36\n - Output consistency score: 30\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 26.0, \"max\": 41.0, \"std\": 5.16}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 48, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 26\n - Labor efficiency index: 58\n - Inventory turnover rating: 33\n - Safety compliance score: 31\n - Energy per unit output rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 58.0, \"std\": 11.15}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 26\n - Labor efficiency index: 58\n - Inventory turnover rating: 33\n - Safety compliance score: 31\n - Energy per unit output rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 58.0, \"std\": 11.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 56, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 26\n - Labor efficiency index: 58\n - Inventory turnover rating: 33\n - Safety compliance score: 31\n - Energy per unit output rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 58.0, \"std\": 11.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 56, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 26\n - Labor efficiency index: 58\n - Inventory turnover rating: 33\n - Safety compliance score: 31\n - Energy per unit output rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 58.0, \"std\": 11.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 56, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 26\n - Labor efficiency index: 58\n - Inventory turnover rating: 33\n - Safety compliance score: 31\n - Energy per unit output rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 58.0, \"std\": 11.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 56, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 63\n - Team productivity index: 59\n - Cycle-time benchmark rating: 57\n - Quality-adjusted throughput score: 51\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.6, \"count\": 5, \"min\": 51.0, \"max\": 63.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 63\n - Team productivity index: 59\n - Cycle-time benchmark rating: 57\n - Quality-adjusted throughput score: 51\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.6, \"count\": 5, \"min\": 51.0, \"max\": 63.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 63\n - Team productivity index: 59\n - Cycle-time benchmark rating: 57\n - Quality-adjusted throughput score: 51\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.6, \"count\": 5, \"min\": 51.0, \"max\": 63.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 63\n - Team productivity index: 59\n - Cycle-time benchmark rating: 57\n - Quality-adjusted throughput score: 51\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.6, \"count\": 5, \"min\": 51.0, \"max\": 63.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 63\n - Team productivity index: 59\n - Cycle-time benchmark rating: 57\n - Quality-adjusted throughput score: 51\n - Lean assessment rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.6, \"count\": 5, \"min\": 51.0, \"max\": 63.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 44\n - Downtime frequency rating: 52\n - Output consistency score: 45\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 44.0, \"max\": 58.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 37, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 44\n - Downtime frequency rating: 52\n - Output consistency score: 45\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 44.0, \"max\": 58.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 37, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 44\n - Downtime frequency rating: 52\n - Output consistency score: 45\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 44.0, \"max\": 58.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 37, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 44\n - Downtime frequency rating: 52\n - Output consistency score: 45\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 44.0, \"max\": 58.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 37, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 15, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 44\n - Downtime frequency rating: 52\n - Output consistency score: 45\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 44.0, \"max\": 58.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 55\n - Inventory turnover rating: 71\n - Safety compliance score: 77\n - Energy per unit output rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 55.0, \"max\": 77.0, \"std\": 7.94}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 41, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 55\n - Inventory turnover rating: 71\n - Safety compliance score: 77\n - Energy per unit output rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 55.0, \"max\": 77.0, \"std\": 7.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 41, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 91, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 55\n - Inventory turnover rating: 71\n - Safety compliance score: 77\n - Energy per unit output rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 55.0, \"max\": 77.0, \"std\": 7.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 91, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 41, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 55\n - Inventory turnover rating: 71\n - Safety compliance score: 77\n - Energy per unit output rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 55.0, \"max\": 77.0, \"std\": 7.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 41, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 91, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 55\n - Inventory turnover rating: 71\n - Safety compliance score: 77\n - Energy per unit output rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 55.0, \"max\": 77.0, \"std\": 7.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 91, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 57\n - Team productivity index: 59\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 48.0, \"max\": 59.0, \"std\": 3.93}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 57\n - Team productivity index: 59\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 48.0, \"max\": 59.0, \"std\": 3.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 57\n - Team productivity index: 59\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 48.0, \"max\": 59.0, \"std\": 3.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 57\n - Team productivity index: 59\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 48.0, \"max\": 59.0, \"std\": 3.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 57\n - Team productivity index: 59\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 48.0, \"max\": 59.0, \"std\": 3.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: 55\n - Downtime frequency rating: 48\n - Output consistency score: 55\n - Continuous improvement rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 48.0, \"max\": 63.0, \"std\": 5.04}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: 55\n - Downtime frequency rating: 48\n - Output consistency score: 55\n - Continuous improvement rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 48.0, \"max\": 63.0, \"std\": 5.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: 55\n - Downtime frequency rating: 48\n - Output consistency score: 55\n - Continuous improvement rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 48.0, \"max\": 63.0, \"std\": 5.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: 55\n - Downtime frequency rating: 48\n - Output consistency score: 55\n - Continuous improvement rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 48.0, \"max\": 63.0, \"std\": 5.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: 55\n - Downtime frequency rating: 48\n - Output consistency score: 55\n - Continuous improvement rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 48.0, \"max\": 63.0, \"std\": 5.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 62\n - Labor efficiency index: 54\n - Inventory turnover rating: 36\n - Safety compliance score: 49\n - Energy per unit output rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 36.0, \"max\": 64.0, \"std\": 10.08}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 62\n - Labor efficiency index: 54\n - Inventory turnover rating: 36\n - Safety compliance score: 49\n - Energy per unit output rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 36.0, \"max\": 64.0, \"std\": 10.08}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 79, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 62\n - Labor efficiency index: 54\n - Inventory turnover rating: 36\n - Safety compliance score: 49\n - Energy per unit output rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 36.0, \"max\": 64.0, \"std\": 10.08}\n\nTool: check_external_reference\nOutput: {\"request_id\": 79, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 62\n - Labor efficiency index: 54\n - Inventory turnover rating: 36\n - Safety compliance score: 49\n - Energy per unit output rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 36.0, \"max\": 64.0, \"std\": 10.08}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 79, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 62\n - Labor efficiency index: 54\n - Inventory turnover rating: 36\n - Safety compliance score: 49\n - Energy per unit output rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 36.0, \"max\": 64.0, \"std\": 10.08}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 79, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 42\n - Team productivity index: 45\n - Cycle-time benchmark rating: 42\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 39.0, \"max\": 45.0, \"std\": 1.9}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 42\n - Team productivity index: 45\n - Cycle-time benchmark rating: 42\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 39.0, \"max\": 45.0, \"std\": 1.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 42\n - Team productivity index: 45\n - Cycle-time benchmark rating: 42\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 39.0, \"max\": 45.0, \"std\": 1.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 42\n - Team productivity index: 45\n - Cycle-time benchmark rating: 42\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 39.0, \"max\": 45.0, \"std\": 1.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 42\n - Team productivity index: 45\n - Cycle-time benchmark rating: 42\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 39.0, \"max\": 45.0, \"std\": 1.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 28\n - Downtime frequency rating: 42\n - Output consistency score: 46\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.6, \"count\": 5, \"min\": 28.0, \"max\": 46.0, \"std\": 7.36}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 28\n - Downtime frequency rating: 42\n - Output consistency score: 46\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.6, \"count\": 5, \"min\": 28.0, \"max\": 46.0, \"std\": 7.36}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 28\n - Downtime frequency rating: 42\n - Output consistency score: 46\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.6, \"count\": 5, \"min\": 28.0, \"max\": 46.0, \"std\": 7.36}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 28\n - Downtime frequency rating: 42\n - Output consistency score: 46\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.6, \"count\": 5, \"min\": 28.0, \"max\": 46.0, \"std\": 7.36}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: 28\n - Downtime frequency rating: 42\n - Output consistency score: 46\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.6, \"count\": 5, \"min\": 28.0, \"max\": 46.0, \"std\": 7.36}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 69\n - Inventory turnover rating: 56\n - Safety compliance score: 50\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 6.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 69\n - Inventory turnover rating: 56\n - Safety compliance score: 50\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 6.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 69\n - Inventory turnover rating: 56\n - Safety compliance score: 50\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 6.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 69\n - Inventory turnover rating: 56\n - Safety compliance score: 50\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 6.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: 59\n - Labor efficiency index: 69\n - Inventory turnover rating: 56\n - Safety compliance score: 50\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 50.0, \"max\": 69.0, \"std\": 6.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: 33\n - Team productivity index: 50\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 45\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.6, \"count\": 5, \"min\": 33.0, \"max\": 50.0, \"std\": 6.22}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: 33\n - Team productivity index: 50\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 45\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.6, \"count\": 5, \"min\": 33.0, \"max\": 50.0, \"std\": 6.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: 33\n - Team productivity index: 50\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 45\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.6, \"count\": 5, \"min\": 33.0, \"max\": 50.0, \"std\": 6.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: 33\n - Team productivity index: 50\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 45\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.6, \"count\": 5, \"min\": 33.0, \"max\": 50.0, \"std\": 6.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: 33\n - Team productivity index: 50\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 45\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.6, \"count\": 5, \"min\": 33.0, \"max\": 50.0, \"std\": 6.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 52\n - Downtime frequency rating: 48\n - Output consistency score: 59\n - Continuous improvement rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 46.0, \"max\": 59.0, \"std\": 4.53}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 52\n - Downtime frequency rating: 48\n - Output consistency score: 59\n - Continuous improvement rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 46.0, \"max\": 59.0, \"std\": 4.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 76, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 52\n - Downtime frequency rating: 48\n - Output consistency score: 59\n - Continuous improvement rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 46.0, \"max\": 59.0, \"std\": 4.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 76, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 52\n - Downtime frequency rating: 48\n - Output consistency score: 59\n - Continuous improvement rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 46.0, \"max\": 59.0, \"std\": 4.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 76, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 46\n - Capacity utilization index: 52\n - Downtime frequency rating: 48\n - Output consistency score: 59\n - Continuous improvement rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.8, \"count\": 5, \"min\": 46.0, \"max\": 59.0, \"std\": 4.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 76, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: 42\n - Labor efficiency index: 55\n - Inventory turnover rating: 61\n - Safety compliance score: 45\n - Energy per unit output rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.6, \"count\": 5, \"min\": 42.0, \"max\": 61.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: 42\n - Labor efficiency index: 55\n - Inventory turnover rating: 61\n - Safety compliance score: 45\n - Energy per unit output rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.6, \"count\": 5, \"min\": 42.0, \"max\": 61.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: 42\n - Labor efficiency index: 55\n - Inventory turnover rating: 61\n - Safety compliance score: 45\n - Energy per unit output rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.6, \"count\": 5, \"min\": 42.0, \"max\": 61.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: 42\n - Labor efficiency index: 55\n - Inventory turnover rating: 61\n - Safety compliance score: 45\n - Energy per unit output rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.6, \"count\": 5, \"min\": 42.0, \"max\": 61.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: 42\n - Labor efficiency index: 55\n - Inventory turnover rating: 61\n - Safety compliance score: 45\n - Energy per unit output rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.6, \"count\": 5, \"min\": 42.0, \"max\": 61.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 53\n - Team productivity index: 44\n - Cycle-time benchmark rating: 53\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 44.0, \"max\": 55.0, \"std\": 4.1}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 12, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 53\n - Team productivity index: 44\n - Cycle-time benchmark rating: 53\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 44.0, \"max\": 55.0, \"std\": 4.1}\n\nTool: check_external_reference\nOutput: {\"request_id\": 12, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 92, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 53\n - Team productivity index: 44\n - Cycle-time benchmark rating: 53\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 44.0, \"max\": 55.0, \"std\": 4.1}\n\nTool: check_external_reference\nOutput: {\"request_id\": 92, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 12, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 53\n - Team productivity index: 44\n - Cycle-time benchmark rating: 53\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 44.0, \"max\": 55.0, \"std\": 4.1}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 12, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 92, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 53\n - Team productivity index: 44\n - Cycle-time benchmark rating: 53\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 44.0, \"max\": 55.0, \"std\": 4.1}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 92, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: 59\n - Capacity utilization index: 59\n - Downtime frequency rating: 55\n - Output consistency score: 63\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.8, \"count\": 5, \"min\": 55.0, \"max\": 63.0, \"std\": 2.56}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 14, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: 59\n - Capacity utilization index: 59\n - Downtime frequency rating: 55\n - Output consistency score: 63\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.8, \"count\": 5, \"min\": 55.0, \"max\": 63.0, \"std\": 2.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 14, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 94, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: 59\n - Capacity utilization index: 59\n - Downtime frequency rating: 55\n - Output consistency score: 63\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.8, \"count\": 5, \"min\": 55.0, \"max\": 63.0, \"std\": 2.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 94, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 14, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: 59\n - Capacity utilization index: 59\n - Downtime frequency rating: 55\n - Output consistency score: 63\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.8, \"count\": 5, \"min\": 55.0, \"max\": 63.0, \"std\": 2.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 14, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 94, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: 59\n - Capacity utilization index: 59\n - Downtime frequency rating: 55\n - Output consistency score: 63\n - Continuous improvement rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.8, \"count\": 5, \"min\": 55.0, \"max\": 63.0, \"std\": 2.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 94, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 73\n - Labor efficiency index: 61\n - Inventory turnover rating: 68\n - Safety compliance score: 74\n - Energy per unit output rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 61.0, \"max\": 74.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 73\n - Labor efficiency index: 61\n - Inventory turnover rating: 68\n - Safety compliance score: 74\n - Energy per unit output rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 61.0, \"max\": 74.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 73\n - Labor efficiency index: 61\n - Inventory turnover rating: 68\n - Safety compliance score: 74\n - Energy per unit output rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 61.0, \"max\": 74.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 73\n - Labor efficiency index: 61\n - Inventory turnover rating: 68\n - Safety compliance score: 74\n - Energy per unit output rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 61.0, \"max\": 74.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 73\n - Labor efficiency index: 61\n - Inventory turnover rating: 68\n - Safety compliance score: 74\n - Energy per unit output rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 61.0, \"max\": 74.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: 42\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 43\n - Lean assessment rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 30.0, \"max\": 43.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: 42\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 43\n - Lean assessment rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 30.0, \"max\": 43.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: 42\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 43\n - Lean assessment rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 30.0, \"max\": 43.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: 42\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 43\n - Lean assessment rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 30.0, \"max\": 43.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: 42\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 43\n - Lean assessment rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 30.0, \"max\": 43.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 18\n - Capacity utilization index: 37\n - Downtime frequency rating: 37\n - Output consistency score: 42\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 5, \"min\": 18.0, \"max\": 42.0, \"std\": 8.74}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 18\n - Capacity utilization index: 37\n - Downtime frequency rating: 37\n - Output consistency score: 42\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 5, \"min\": 18.0, \"max\": 42.0, \"std\": 8.74}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 18\n - Capacity utilization index: 37\n - Downtime frequency rating: 37\n - Output consistency score: 42\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 5, \"min\": 18.0, \"max\": 42.0, \"std\": 8.74}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 18\n - Capacity utilization index: 37\n - Downtime frequency rating: 37\n - Output consistency score: 42\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 5, \"min\": 18.0, \"max\": 42.0, \"std\": 8.74}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 18\n - Capacity utilization index: 37\n - Downtime frequency rating: 37\n - Output consistency score: 42\n - Continuous improvement rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 5, \"min\": 18.0, \"max\": 42.0, \"std\": 8.74}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 49\n - Labor efficiency index: 47\n - Inventory turnover rating: 56\n - Safety compliance score: 59\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 47.0, \"max\": 61.0, \"std\": 5.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 49\n - Labor efficiency index: 47\n - Inventory turnover rating: 56\n - Safety compliance score: 59\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 47.0, \"max\": 61.0, \"std\": 5.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 95, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 49\n - Labor efficiency index: 47\n - Inventory turnover rating: 56\n - Safety compliance score: 59\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 47.0, \"max\": 61.0, \"std\": 5.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 95, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 49\n - Labor efficiency index: 47\n - Inventory turnover rating: 56\n - Safety compliance score: 59\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 47.0, \"max\": 61.0, \"std\": 5.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 95, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: 49\n - Labor efficiency index: 47\n - Inventory turnover rating: 56\n - Safety compliance score: 59\n - Energy per unit output rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.4, \"count\": 5, \"min\": 47.0, \"max\": 61.0, \"std\": 5.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 95, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 32\n - Team productivity index: 39\n - Cycle-time benchmark rating: 21\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.8, \"count\": 5, \"min\": 21.0, \"max\": 42.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 32\n - Team productivity index: 39\n - Cycle-time benchmark rating: 21\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.8, \"count\": 5, \"min\": 21.0, \"max\": 42.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 32\n - Team productivity index: 39\n - Cycle-time benchmark rating: 21\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.8, \"count\": 5, \"min\": 21.0, \"max\": 42.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 32\n - Team productivity index: 39\n - Cycle-time benchmark rating: 21\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.8, \"count\": 5, \"min\": 21.0, \"max\": 42.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 32\n - Team productivity index: 39\n - Cycle-time benchmark rating: 21\n - Quality-adjusted throughput score: 42\n - Lean assessment rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.8, \"count\": 5, \"min\": 21.0, \"max\": 42.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 62\n - Downtime frequency rating: 66\n - Output consistency score: 66\n - Continuous improvement rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 55.0, \"max\": 66.0, \"std\": 4.05}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 62\n - Downtime frequency rating: 66\n - Output consistency score: 66\n - Continuous improvement rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 55.0, \"max\": 66.0, \"std\": 4.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 62\n - Downtime frequency rating: 66\n - Output consistency score: 66\n - Continuous improvement rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 55.0, \"max\": 66.0, \"std\": 4.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 62\n - Downtime frequency rating: 66\n - Output consistency score: 66\n - Continuous improvement rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 55.0, \"max\": 66.0, \"std\": 4.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 55\n - Capacity utilization index: 62\n - Downtime frequency rating: 66\n - Output consistency score: 66\n - Continuous improvement rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 55.0, \"max\": 66.0, \"std\": 4.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 53\n - Labor efficiency index: 31\n - Inventory turnover rating: 47\n - Safety compliance score: 33\n - Energy per unit output rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 5, \"min\": 31.0, \"max\": 53.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 53\n - Labor efficiency index: 31\n - Inventory turnover rating: 47\n - Safety compliance score: 33\n - Energy per unit output rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 5, \"min\": 31.0, \"max\": 53.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 53\n - Labor efficiency index: 31\n - Inventory turnover rating: 47\n - Safety compliance score: 33\n - Energy per unit output rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 5, \"min\": 31.0, \"max\": 53.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 53\n - Labor efficiency index: 31\n - Inventory turnover rating: 47\n - Safety compliance score: 33\n - Energy per unit output rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 5, \"min\": 31.0, \"max\": 53.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 53\n - Labor efficiency index: 31\n - Inventory turnover rating: 47\n - Safety compliance score: 33\n - Energy per unit output rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 5, \"min\": 31.0, \"max\": 53.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 52\n - Team productivity index: 43\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 54\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 36.0, \"max\": 54.0, \"std\": 6.93}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 52\n - Team productivity index: 43\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 54\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 36.0, \"max\": 54.0, \"std\": 6.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 52\n - Team productivity index: 43\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 54\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 36.0, \"max\": 54.0, \"std\": 6.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 88, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 52\n - Team productivity index: 43\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 54\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 36.0, \"max\": 54.0, \"std\": 6.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-e-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "easy", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: 52\n - Team productivity index: 43\n - Cycle-time benchmark rating: 40\n - Quality-adjusted throughput score: 54\n - Lean assessment rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 36.0, \"max\": 54.0, \"std\": 6.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 88, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 63\n - Downtime frequency rating: [data not available]\n - Output consistency score: 21\n - Continuous improvement rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 15.0, \"max\": 63.0, \"std\": 21.35}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 63\n - Downtime frequency rating: [data not available]\n - Output consistency score: 21\n - Continuous improvement rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 15.0, \"max\": 63.0, \"std\": 21.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 63\n - Downtime frequency rating: [data not available]\n - Output consistency score: 21\n - Continuous improvement rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 15.0, \"max\": 63.0, \"std\": 21.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 63\n - Downtime frequency rating: [data not available]\n - Output consistency score: 21\n - Continuous improvement rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 15.0, \"max\": 63.0, \"std\": 21.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 63\n - Downtime frequency rating: [data not available]\n - Output consistency score: 21\n - Continuous improvement rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 15.0, \"max\": 63.0, \"std\": 21.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 67\n - Labor efficiency index: 69\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 46\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 46.0, \"max\": 69.0, \"std\": 10.4}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 67\n - Labor efficiency index: 69\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 46\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 46.0, \"max\": 69.0, \"std\": 10.4}\n\nTool: check_external_reference\nOutput: {\"request_id\": 36, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 67\n - Labor efficiency index: 69\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 46\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 46.0, \"max\": 69.0, \"std\": 10.4}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 67\n - Labor efficiency index: 69\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 46\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 46.0, \"max\": 69.0, \"std\": 10.4}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 36, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 67\n - Labor efficiency index: 69\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 46\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 46.0, \"max\": 69.0, \"std\": 10.4}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 46\n - Team productivity index: 58\n - Cycle-time benchmark rating: 63\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 63.0, \"std\": 7.13}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 46\n - Team productivity index: 58\n - Cycle-time benchmark rating: 63\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 63.0, \"std\": 7.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 46\n - Team productivity index: 58\n - Cycle-time benchmark rating: 63\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 63.0, \"std\": 7.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 46\n - Team productivity index: 58\n - Cycle-time benchmark rating: 63\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 63.0, \"std\": 7.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: 46\n - Team productivity index: 58\n - Cycle-time benchmark rating: 63\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 63.0, \"std\": 7.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 32\n - Capacity utilization index: 34\n - Downtime frequency rating: 45\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 32.0, \"max\": 45.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 32\n - Capacity utilization index: 34\n - Downtime frequency rating: 45\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 32.0, \"max\": 45.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 32\n - Capacity utilization index: 34\n - Downtime frequency rating: 45\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 32.0, \"max\": 45.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 53, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 32\n - Capacity utilization index: 34\n - Downtime frequency rating: 45\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 32.0, \"max\": 45.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: 32\n - Capacity utilization index: 34\n - Downtime frequency rating: 45\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 3, \"min\": 32.0, \"max\": 45.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 53, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 52\n - Safety compliance score: 44\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 30.0, \"max\": 52.0, \"std\": 9.09}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 52\n - Safety compliance score: 44\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 30.0, \"max\": 52.0, \"std\": 9.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 52\n - Safety compliance score: 44\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 30.0, \"max\": 52.0, \"std\": 9.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 47, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 52\n - Safety compliance score: 44\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 30.0, \"max\": 52.0, \"std\": 9.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 52\n - Safety compliance score: 44\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 3, \"min\": 30.0, \"max\": 52.0, \"std\": 9.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 47, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 71\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 31\n - Quality-adjusted throughput score: 64\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.33, \"count\": 3, \"min\": 31.0, \"max\": 71.0, \"std\": 17.44}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 71\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 31\n - Quality-adjusted throughput score: 64\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.33, \"count\": 3, \"min\": 31.0, \"max\": 71.0, \"std\": 17.44}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 71\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 31\n - Quality-adjusted throughput score: 64\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.33, \"count\": 3, \"min\": 31.0, \"max\": 71.0, \"std\": 17.44}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 71\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 31\n - Quality-adjusted throughput score: 64\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.33, \"count\": 3, \"min\": 31.0, \"max\": 71.0, \"std\": 17.44}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: 71\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 31\n - Quality-adjusted throughput score: 64\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.33, \"count\": 3, \"min\": 31.0, \"max\": 71.0, \"std\": 17.44}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 70\n - Continuous improvement rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 70.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 70\n - Continuous improvement rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 70.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 47, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 70\n - Continuous improvement rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 70.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 70\n - Continuous improvement rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 70.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 47, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Workflow automation score: 51\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 70\n - Continuous improvement rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 46.0, \"max\": 70.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 57\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 0\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 0.0, \"max\": 57.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 57\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 0\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 0.0, \"max\": 57.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 57\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 0\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 0.0, \"max\": 57.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 57\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 0\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 0.0, \"max\": 57.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Equipment effectiveness score: 57\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 0\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 0.0, \"max\": 57.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 30.0, \"max\": 55.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 30.0, \"max\": 55.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 30.0, \"max\": 55.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 30.0, \"max\": 55.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Process audit efficiency score: 30\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: 55\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 30.0, \"max\": 55.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 87\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 28.0, \"max\": 87.0, \"std\": 25.62}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 87\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 28.0, \"max\": 87.0, \"std\": 25.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 87\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 28.0, \"max\": 87.0, \"std\": 25.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 87\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 28.0, \"max\": 87.0, \"std\": 25.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Workflow automation score: 39\n - Capacity utilization index: [data not available]\n - Downtime frequency rating: [data not available]\n - Output consistency score: 87\n - Continuous improvement rating: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 28.0, \"max\": 87.0, \"std\": 25.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 65\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 43\n - Energy per unit output rating: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 65\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 43\n - Energy per unit output rating: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 65\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 43\n - Energy per unit output rating: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 65\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 43\n - Energy per unit output rating: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 65\n - Inventory turnover rating: [data not available]\n - Safety compliance score: 43\n - Energy per unit output rating: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 61\n - Lean assessment rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 3, \"min\": 44.0, \"max\": 69.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 61\n - Lean assessment rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 3, \"min\": 44.0, \"max\": 69.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 58, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 61\n - Lean assessment rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 3, \"min\": 44.0, \"max\": 69.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 58, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 61\n - Lean assessment rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 3, \"min\": 44.0, \"max\": 69.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 58, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 44\n - Quality-adjusted throughput score: 61\n - Lean assessment rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.0, \"count\": 3, \"min\": 44.0, \"max\": 69.0, \"std\": 10.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 58, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 66\n - Capacity utilization index: 20\n - Downtime frequency rating: 59\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 20.0, \"max\": 66.0, \"std\": 20.24}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 66\n - Capacity utilization index: 20\n - Downtime frequency rating: 59\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 20.0, \"max\": 66.0, \"std\": 20.24}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 66\n - Capacity utilization index: 20\n - Downtime frequency rating: 59\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 20.0, \"max\": 66.0, \"std\": 20.24}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 66\n - Capacity utilization index: 20\n - Downtime frequency rating: 59\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 20.0, \"max\": 66.0, \"std\": 20.24}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Workflow automation score: 66\n - Capacity utilization index: 20\n - Downtime frequency rating: 59\n - Output consistency score: [data not available]\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 20.0, \"max\": 66.0, \"std\": 20.24}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 33\n - Safety compliance score: 27\n - Energy per unit output rating: 14\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.67, \"count\": 3, \"min\": 14.0, \"max\": 33.0, \"std\": 7.93}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 25, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 33\n - Safety compliance score: 27\n - Energy per unit output rating: 14\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.67, \"count\": 3, \"min\": 14.0, \"max\": 33.0, \"std\": 7.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 25, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 33\n - Safety compliance score: 27\n - Energy per unit output rating: 14\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.67, \"count\": 3, \"min\": 14.0, \"max\": 33.0, \"std\": 7.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 25, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 33\n - Safety compliance score: 27\n - Energy per unit output rating: 14\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.67, \"count\": 3, \"min\": 14.0, \"max\": 33.0, \"std\": 7.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 25, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: [data not available]\n - Inventory turnover rating: 33\n - Safety compliance score: 27\n - Energy per unit output rating: 14\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 24.67, \"count\": 3, \"min\": 14.0, \"max\": 33.0, \"std\": 7.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 25, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 49\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: [data not available]\n - Quality-adjusted throughput score: 47\n - Lean assessment rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 47.0, \"max\": 49.0, \"std\": 0.94}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 49\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: [data not available]\n - Quality-adjusted throughput score: 47\n - Lean assessment rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 47.0, \"max\": 49.0, \"std\": 0.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 49\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: [data not available]\n - Quality-adjusted throughput score: 47\n - Lean assessment rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 47.0, \"max\": 49.0, \"std\": 0.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 49\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: [data not available]\n - Quality-adjusted throughput score: 47\n - Lean assessment rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 47.0, \"max\": 49.0, \"std\": 0.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Process audit efficiency score: 49\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: [data not available]\n - Quality-adjusted throughput score: 47\n - Lean assessment rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 47.0, \"max\": 49.0, \"std\": 0.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 23\n - Downtime frequency rating: [data not available]\n - Output consistency score: 29\n - Continuous improvement rating: 99\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 23.0, \"max\": 99.0, \"std\": 34.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 10, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 23\n - Downtime frequency rating: [data not available]\n - Output consistency score: 29\n - Continuous improvement rating: 99\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 23.0, \"max\": 99.0, \"std\": 34.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 10, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 60, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 23\n - Downtime frequency rating: [data not available]\n - Output consistency score: 29\n - Continuous improvement rating: 99\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 23.0, \"max\": 99.0, \"std\": 34.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 60, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 10, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 23\n - Downtime frequency rating: [data not available]\n - Output consistency score: 29\n - Continuous improvement rating: 99\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 23.0, \"max\": 99.0, \"std\": 34.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 10, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 60, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 23\n - Downtime frequency rating: [data not available]\n - Output consistency score: 29\n - Continuous improvement rating: 99\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 23.0, \"max\": 99.0, \"std\": 34.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 60, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 61\n - Labor efficiency index: 51\n - Inventory turnover rating: 32\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 32.0, \"max\": 61.0, \"std\": 12.03}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 61\n - Labor efficiency index: 51\n - Inventory turnover rating: 32\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 32.0, \"max\": 61.0, \"std\": 12.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 61\n - Labor efficiency index: 51\n - Inventory turnover rating: 32\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 32.0, \"max\": 61.0, \"std\": 12.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 61\n - Labor efficiency index: 51\n - Inventory turnover rating: 32\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 32.0, \"max\": 61.0, \"std\": 12.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Equipment effectiveness score: 61\n - Labor efficiency index: 51\n - Inventory turnover rating: 32\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 32.0, \"max\": 61.0, \"std\": 12.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 54\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 51\n - Quality-adjusted throughput score: 91\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.33, \"count\": 3, \"min\": 51.0, \"max\": 91.0, \"std\": 18.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 41, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 54\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 51\n - Quality-adjusted throughput score: 91\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.33, \"count\": 3, \"min\": 51.0, \"max\": 91.0, \"std\": 18.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 41, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 91, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 54\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 51\n - Quality-adjusted throughput score: 91\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.33, \"count\": 3, \"min\": 51.0, \"max\": 91.0, \"std\": 18.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 91, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 41, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 54\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 51\n - Quality-adjusted throughput score: 91\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.33, \"count\": 3, \"min\": 51.0, \"max\": 91.0, \"std\": 18.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 41, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 91, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Process audit efficiency score: 54\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 51\n - Quality-adjusted throughput score: 91\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.33, \"count\": 3, \"min\": 51.0, \"max\": 91.0, \"std\": 18.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 91, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 36\n - Capacity utilization index: 83\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.0, \"count\": 3, \"min\": 36.0, \"max\": 83.0, \"std\": 20.7}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 42, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 36\n - Capacity utilization index: 83\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.0, \"count\": 3, \"min\": 36.0, \"max\": 83.0, \"std\": 20.7}\n\nTool: check_external_reference\nOutput: {\"request_id\": 42, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 92, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 36\n - Capacity utilization index: 83\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.0, \"count\": 3, \"min\": 36.0, \"max\": 83.0, \"std\": 20.7}\n\nTool: check_external_reference\nOutput: {\"request_id\": 92, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 42, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 36\n - Capacity utilization index: 83\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.0, \"count\": 3, \"min\": 36.0, \"max\": 83.0, \"std\": 20.7}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 42, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 92, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Workflow automation score: 36\n - Capacity utilization index: 83\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.0, \"count\": 3, \"min\": 36.0, \"max\": 83.0, \"std\": 20.7}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 92, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 59\n - Inventory turnover rating: 39\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 39.0, \"max\": 73.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 59\n - Inventory turnover rating: 39\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 39.0, \"max\": 73.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 59\n - Inventory turnover rating: 39\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 39.0, \"max\": 73.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 59\n - Inventory turnover rating: 39\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 39.0, \"max\": 73.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 25, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 59\n - Inventory turnover rating: 39\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 39.0, \"max\": 73.0, \"std\": 13.95}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 79\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 80\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.67, \"count\": 3, \"min\": 56.0, \"max\": 80.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 79\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 80\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.67, \"count\": 3, \"min\": 56.0, \"max\": 80.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 79\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 80\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.67, \"count\": 3, \"min\": 56.0, \"max\": 80.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 79\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 80\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.67, \"count\": 3, \"min\": 56.0, \"max\": 80.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Process audit efficiency score: 79\n - Team productivity index: [data not available]\n - Cycle-time benchmark rating: 80\n - Quality-adjusted throughput score: 56\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.67, \"count\": 3, \"min\": 56.0, \"max\": 80.0, \"std\": 11.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 43\n - Capacity utilization index: 50\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 43.0, \"max\": 53.0, \"std\": 4.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 43\n - Capacity utilization index: 50\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 43.0, \"max\": 53.0, \"std\": 4.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 43\n - Capacity utilization index: 50\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 43.0, \"max\": 53.0, \"std\": 4.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 93, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 43\n - Capacity utilization index: 50\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 43.0, \"max\": 53.0, \"std\": 4.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Workflow automation score: 43\n - Capacity utilization index: 50\n - Downtime frequency rating: [data not available]\n - Output consistency score: [data not available]\n - Continuous improvement rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 43.0, \"max\": 53.0, \"std\": 4.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 93, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 74\n - Labor efficiency index: 56\n - Inventory turnover rating: 47\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 47.0, \"max\": 74.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 74\n - Labor efficiency index: 56\n - Inventory turnover rating: 47\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 47.0, \"max\": 74.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 74\n - Labor efficiency index: 56\n - Inventory turnover rating: 47\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 47.0, \"max\": 74.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 74\n - Labor efficiency index: 56\n - Inventory turnover rating: 47\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 47.0, \"max\": 74.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA logistics hub is evaluating warehouse operations efficiency. The following ratings (0–100) were collected from independent auditors.\n\nEvidence:\n - Equipment effectiveness score: 74\n - Labor efficiency index: 56\n - Inventory turnover rating: 47\n - Safety compliance score: [data not available]\n - Energy per unit output rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 47.0, \"max\": 74.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 81\n - Cycle-time benchmark rating: 35\n - Quality-adjusted throughput score: 37\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 35.0, \"max\": 81.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 11, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 81\n - Cycle-time benchmark rating: 35\n - Quality-adjusted throughput score: 37\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 35.0, \"max\": 81.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 11, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 91, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 81\n - Cycle-time benchmark rating: 35\n - Quality-adjusted throughput score: 37\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 35.0, \"max\": 81.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 91, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 11, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 81\n - Cycle-time benchmark rating: 35\n - Quality-adjusted throughput score: 37\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 35.0, \"max\": 81.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 11, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 91, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital is reviewing surgical suite turnaround efficiency. Five departmental assessments are summarized below.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 81\n - Cycle-time benchmark rating: 35\n - Quality-adjusted throughput score: 37\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 35.0, \"max\": 81.0, \"std\": 21.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 91, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 58\n - Capacity utilization index: 11\n - Downtime frequency rating: [data not available]\n - Output consistency score: 52\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 11.0, \"max\": 58.0, \"std\": 20.89}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 58\n - Capacity utilization index: 11\n - Downtime frequency rating: [data not available]\n - Output consistency score: 52\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 11.0, \"max\": 58.0, \"std\": 20.89}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 98, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 58\n - Capacity utilization index: 11\n - Downtime frequency rating: [data not available]\n - Output consistency score: 52\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 11.0, \"max\": 58.0, \"std\": 20.89}\n\nTool: check_external_reference\nOutput: {\"request_id\": 98, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 58\n - Capacity utilization index: 11\n - Downtime frequency rating: [data not available]\n - Output consistency score: 52\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 11.0, \"max\": 58.0, \"std\": 20.89}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 98, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA call center is estimating its service efficiency index. Independent evaluations produced the following scores.\n\nEvidence:\n - Workflow automation score: 58\n - Capacity utilization index: 11\n - Downtime frequency rating: [data not available]\n - Output consistency score: 52\n - Continuous improvement rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 11.0, \"max\": 58.0, \"std\": 20.89}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 98, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 46\n - Inventory turnover rating: 61\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.16}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 46\n - Inventory turnover rating: 61\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.16}\n\nTool: check_external_reference\nOutput: {\"request_id\": 7, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 46\n - Inventory turnover rating: 61\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.16}\n\nTool: check_external_reference\nOutput: {\"request_id\": 87, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 46\n - Inventory turnover rating: 61\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.16}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 7, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA semiconductor fabrication plant is assessing production line efficiency. Five audit results are summarized below.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 46\n - Inventory turnover rating: 61\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 46.0, \"max\": 61.0, \"std\": 6.16}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 87, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 48\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 65\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 48.0, \"max\": 65.0, \"std\": 6.98}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 10, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 48\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 65\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 48.0, \"max\": 65.0, \"std\": 6.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 10, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 90, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 48\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 65\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 48.0, \"max\": 65.0, \"std\": 6.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 90, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 10, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 48\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 65\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 48.0, \"max\": 65.0, \"std\": 6.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 10, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 90, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food processing facility is evaluating throughput and quality efficiency. Independent assessors provided the following ratings.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 48\n - Cycle-time benchmark rating: 58\n - Quality-adjusted throughput score: 65\n - Lean assessment rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 48.0, \"max\": 65.0, \"std\": 6.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 90, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the ratings above, what is your best estimate for the efficiency score on a 0–100 scale?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 18\n - Downtime frequency rating: [data not available]\n - Output consistency score: 71\n - Continuous improvement rating: 91\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 18.0, \"max\": 91.0, \"std\": 30.8}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 18\n - Downtime frequency rating: [data not available]\n - Output consistency score: 71\n - Continuous improvement rating: 91\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 18.0, \"max\": 91.0, \"std\": 30.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 18\n - Downtime frequency rating: [data not available]\n - Output consistency score: 71\n - Continuous improvement rating: 91\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 18.0, \"max\": 91.0, \"std\": 30.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 18\n - Downtime frequency rating: [data not available]\n - Output consistency score: 71\n - Continuous improvement rating: 91\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 18.0, \"max\": 91.0, \"std\": 30.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn automobile assembly plant is reviewing its lean operations performance. The following scores were collected.\n\nEvidence:\n - Workflow automation score: [data not available]\n - Capacity utilization index: 18\n - Downtime frequency rating: [data not available]\n - Output consistency score: 71\n - Continuous improvement rating: 91\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 18.0, \"max\": 91.0, \"std\": 30.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite operational efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 54\n - Inventory turnover rating: 70\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 31.0, \"max\": 70.0, \"std\": 16.01}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 54\n - Inventory turnover rating: 70\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 31.0, \"max\": 70.0, \"std\": 16.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 54\n - Inventory turnover rating: 70\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 31.0, \"max\": 70.0, \"std\": 16.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 54\n - Inventory turnover rating: 70\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 31.0, \"max\": 70.0, \"std\": 16.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA distribution center is measuring order fulfillment efficiency. Five performance evaluations are available.\n\nEvidence:\n - Equipment effectiveness score: [data not available]\n - Labor efficiency index: 54\n - Inventory turnover rating: 70\n - Safety compliance score: [data not available]\n - Energy per unit output rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 31.0, \"max\": 70.0, \"std\": 16.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall efficiency score (0–100) do you estimate based on these evaluations?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 47\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 29.0, \"max\": 47.0, \"std\": 7.35}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 47\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 29.0, \"max\": 47.0, \"std\": 7.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 47\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 29.0, \"max\": 47.0, \"std\": 7.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 47\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 29.0, \"max\": 47.0, \"std\": 7.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-operations_time-h-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 40, "difficulty": "hard", "domain": "operations_time", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn operations manager is assessing overall process efficiency for a manufacturing line. Five independent audit ratings are available.\n\nEvidence:\n - Process audit efficiency score: [data not available]\n - Team productivity index: 47\n - Cycle-time benchmark rating: 38\n - Quality-adjusted throughput score: [data not available]\n - Lean assessment rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 29.0, \"max\": 47.0, \"std\": 7.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall operational efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 42\n - Carrier compliance index: 52\n - Load factor optimization rating: 50\n - Claims resolution score: 40\n - Last-mile performance rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 40.0, \"max\": 52.0, \"std\": 4.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 40, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 42\n - Carrier compliance index: 52\n - Load factor optimization rating: 50\n - Claims resolution score: 40\n - Last-mile performance rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 40.0, \"max\": 52.0, \"std\": 4.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 40, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 42\n - Carrier compliance index: 52\n - Load factor optimization rating: 50\n - Claims resolution score: 40\n - Last-mile performance rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 40.0, \"max\": 52.0, \"std\": 4.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 40, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 42\n - Carrier compliance index: 52\n - Load factor optimization rating: 50\n - Claims resolution score: 40\n - Last-mile performance rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 40.0, \"max\": 52.0, \"std\": 4.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 40, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 42\n - Carrier compliance index: 52\n - Load factor optimization rating: 50\n - Claims resolution score: 40\n - Last-mile performance rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 40.0, \"max\": 52.0, \"std\": 4.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 69\n - Network coverage index: 76\n - Temperature control compliance rating: 59\n - Documentation accuracy score: 63\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 59.0, \"max\": 76.0, \"std\": 6.11}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 54, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 69\n - Network coverage index: 76\n - Temperature control compliance rating: 59\n - Documentation accuracy score: 63\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 59.0, \"max\": 76.0, \"std\": 6.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 54, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 69\n - Network coverage index: 76\n - Temperature control compliance rating: 59\n - Documentation accuracy score: 63\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 59.0, \"max\": 76.0, \"std\": 6.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 54, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 69\n - Network coverage index: 76\n - Temperature control compliance rating: 59\n - Documentation accuracy score: 63\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 59.0, \"max\": 76.0, \"std\": 6.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 54, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 69\n - Network coverage index: 76\n - Temperature control compliance rating: 59\n - Documentation accuracy score: 63\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.8, \"count\": 5, \"min\": 59.0, \"max\": 76.0, \"std\": 6.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 34\n - Route optimization index: 48\n - Fleet utilization rating: 46\n - Customer satisfaction (logistics) score: 63\n - Damage/loss incident inverse score: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 9.6}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 34\n - Route optimization index: 48\n - Fleet utilization rating: 46\n - Customer satisfaction (logistics) score: 63\n - Damage/loss incident inverse score: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 9.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 34\n - Route optimization index: 48\n - Fleet utilization rating: 46\n - Customer satisfaction (logistics) score: 63\n - Damage/loss incident inverse score: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 9.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 62, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 34\n - Route optimization index: 48\n - Fleet utilization rating: 46\n - Customer satisfaction (logistics) score: 63\n - Damage/loss incident inverse score: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 9.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 34\n - Route optimization index: 48\n - Fleet utilization rating: 46\n - Customer satisfaction (logistics) score: 63\n - Damage/loss incident inverse score: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 9.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 62, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 52\n - Carrier compliance index: 65\n - Load factor optimization rating: 58\n - Claims resolution score: 63\n - Last-mile performance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 52.0, \"max\": 65.0, \"std\": 4.6}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 43, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 52\n - Carrier compliance index: 65\n - Load factor optimization rating: 58\n - Claims resolution score: 63\n - Last-mile performance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 52.0, \"max\": 65.0, \"std\": 4.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 43, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 73, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 52\n - Carrier compliance index: 65\n - Load factor optimization rating: 58\n - Claims resolution score: 63\n - Last-mile performance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 52.0, \"max\": 65.0, \"std\": 4.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 73, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 43, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 52\n - Carrier compliance index: 65\n - Load factor optimization rating: 58\n - Claims resolution score: 63\n - Last-mile performance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 52.0, \"max\": 65.0, \"std\": 4.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 43, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 73, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 52\n - Carrier compliance index: 65\n - Load factor optimization rating: 58\n - Claims resolution score: 63\n - Last-mile performance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 5, \"min\": 52.0, \"max\": 65.0, \"std\": 4.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 73, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 43\n - Network coverage index: 47\n - Temperature control compliance rating: 47\n - Documentation accuracy score: 39\n - Cross-docking efficiency rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 50.0, \"std\": 3.82}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 43\n - Network coverage index: 47\n - Temperature control compliance rating: 47\n - Documentation accuracy score: 39\n - Cross-docking efficiency rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 50.0, \"std\": 3.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 58, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 43\n - Network coverage index: 47\n - Temperature control compliance rating: 47\n - Documentation accuracy score: 39\n - Cross-docking efficiency rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 50.0, \"std\": 3.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 58, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 43\n - Network coverage index: 47\n - Temperature control compliance rating: 47\n - Documentation accuracy score: 39\n - Cross-docking efficiency rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 50.0, \"std\": 3.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 58, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 43\n - Network coverage index: 47\n - Temperature control compliance rating: 47\n - Documentation accuracy score: 39\n - Cross-docking efficiency rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 50.0, \"std\": 3.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 58, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 40\n - Route optimization index: 70\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 40.0, \"max\": 71.0, \"std\": 12.6}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 40\n - Route optimization index: 70\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 40.0, \"max\": 71.0, \"std\": 12.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 40\n - Route optimization index: 70\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 40.0, \"max\": 71.0, \"std\": 12.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 40\n - Route optimization index: 70\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 40.0, \"max\": 71.0, \"std\": 12.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 40\n - Route optimization index: 70\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 40.0, \"max\": 71.0, \"std\": 12.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 24\n - Carrier compliance index: 30\n - Load factor optimization rating: 29\n - Claims resolution score: 26\n - Last-mile performance rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.2, \"count\": 5, \"min\": 24.0, \"max\": 37.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 24\n - Carrier compliance index: 30\n - Load factor optimization rating: 29\n - Claims resolution score: 26\n - Last-mile performance rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.2, \"count\": 5, \"min\": 24.0, \"max\": 37.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 45, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 24\n - Carrier compliance index: 30\n - Load factor optimization rating: 29\n - Claims resolution score: 26\n - Last-mile performance rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.2, \"count\": 5, \"min\": 24.0, \"max\": 37.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 24\n - Carrier compliance index: 30\n - Load factor optimization rating: 29\n - Claims resolution score: 26\n - Last-mile performance rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.2, \"count\": 5, \"min\": 24.0, \"max\": 37.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 45, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 24\n - Carrier compliance index: 30\n - Load factor optimization rating: 29\n - Claims resolution score: 26\n - Last-mile performance rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.2, \"count\": 5, \"min\": 24.0, \"max\": 37.0, \"std\": 4.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 44\n - Network coverage index: 30\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 49\n - Cross-docking efficiency rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 30.0, \"max\": 53.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 44\n - Network coverage index: 30\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 49\n - Cross-docking efficiency rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 30.0, \"max\": 53.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 44\n - Network coverage index: 30\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 49\n - Cross-docking efficiency rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 30.0, \"max\": 53.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 44\n - Network coverage index: 30\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 49\n - Cross-docking efficiency rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 30.0, \"max\": 53.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 44\n - Network coverage index: 30\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 49\n - Cross-docking efficiency rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 30.0, \"max\": 53.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 32\n - Fleet utilization rating: 18\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 5, \"min\": 18.0, \"max\": 47.0, \"std\": 9.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 32\n - Fleet utilization rating: 18\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 5, \"min\": 18.0, \"max\": 47.0, \"std\": 9.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 54, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 32\n - Fleet utilization rating: 18\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 5, \"min\": 18.0, \"max\": 47.0, \"std\": 9.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 54, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 32\n - Fleet utilization rating: 18\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 5, \"min\": 18.0, \"max\": 47.0, \"std\": 9.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 54, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 32\n - Fleet utilization rating: 18\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 5, \"min\": 18.0, \"max\": 47.0, \"std\": 9.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 54, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 40\n - Carrier compliance index: 65\n - Load factor optimization rating: 33\n - Claims resolution score: 46\n - Last-mile performance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 33.0, \"max\": 65.0, \"std\": 11.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 35, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 40\n - Carrier compliance index: 65\n - Load factor optimization rating: 33\n - Claims resolution score: 46\n - Last-mile performance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 33.0, \"max\": 65.0, \"std\": 11.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 35, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 65, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 40\n - Carrier compliance index: 65\n - Load factor optimization rating: 33\n - Claims resolution score: 46\n - Last-mile performance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 33.0, \"max\": 65.0, \"std\": 11.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 65, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 35, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 40\n - Carrier compliance index: 65\n - Load factor optimization rating: 33\n - Claims resolution score: 46\n - Last-mile performance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 33.0, \"max\": 65.0, \"std\": 11.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 35, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 65, "offset": 15, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 40\n - Carrier compliance index: 65\n - Load factor optimization rating: 33\n - Claims resolution score: 46\n - Last-mile performance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 33.0, \"max\": 65.0, \"std\": 11.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 65, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: 39\n - Network coverage index: 37\n - Temperature control compliance rating: 51\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 15.0, \"max\": 51.0, \"std\": 11.62}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: 39\n - Network coverage index: 37\n - Temperature control compliance rating: 51\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 15.0, \"max\": 51.0, \"std\": 11.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 63, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: 39\n - Network coverage index: 37\n - Temperature control compliance rating: 51\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 15.0, \"max\": 51.0, \"std\": 11.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 63, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: 39\n - Network coverage index: 37\n - Temperature control compliance rating: 51\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 15.0, \"max\": 51.0, \"std\": 11.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 63, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: 39\n - Network coverage index: 37\n - Temperature control compliance rating: 51\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 15\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 15.0, \"max\": 51.0, \"std\": 11.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 63, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 39\n - Fleet utilization rating: 15\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.8, \"count\": 5, \"min\": 15.0, \"max\": 47.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 7, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 39\n - Fleet utilization rating: 15\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.8, \"count\": 5, \"min\": 15.0, \"max\": 47.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 7, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 57, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 39\n - Fleet utilization rating: 15\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.8, \"count\": 5, \"min\": 15.0, \"max\": 47.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 57, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 7, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 39\n - Fleet utilization rating: 15\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.8, \"count\": 5, \"min\": 15.0, \"max\": 47.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 7, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 57, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 35\n - Route optimization index: 39\n - Fleet utilization rating: 15\n - Customer satisfaction (logistics) score: 47\n - Damage/loss incident inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 34.8, \"count\": 5, \"min\": 15.0, \"max\": 47.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 57, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: 66\n - Carrier compliance index: 51\n - Load factor optimization rating: 49\n - Claims resolution score: 39\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: 66\n - Carrier compliance index: 51\n - Load factor optimization rating: 49\n - Claims resolution score: 39\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 76, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: 66\n - Carrier compliance index: 51\n - Load factor optimization rating: 49\n - Claims resolution score: 39\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 76, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: 66\n - Carrier compliance index: 51\n - Load factor optimization rating: 49\n - Claims resolution score: 39\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 76, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: 66\n - Carrier compliance index: 51\n - Load factor optimization rating: 49\n - Claims resolution score: 39\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 76, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 37\n - Network coverage index: 41\n - Temperature control compliance rating: 31\n - Documentation accuracy score: 21\n - Cross-docking efficiency rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.8, \"count\": 5, \"min\": 21.0, \"max\": 41.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 32, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 6, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 37\n - Network coverage index: 41\n - Temperature control compliance rating: 31\n - Documentation accuracy score: 21\n - Cross-docking efficiency rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.8, \"count\": 5, \"min\": 21.0, \"max\": 41.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 6, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 32, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 56, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 37\n - Network coverage index: 41\n - Temperature control compliance rating: 31\n - Documentation accuracy score: 21\n - Cross-docking efficiency rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.8, \"count\": 5, \"min\": 21.0, \"max\": 41.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 56, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 32, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 6, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 37\n - Network coverage index: 41\n - Temperature control compliance rating: 31\n - Documentation accuracy score: 21\n - Cross-docking efficiency rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.8, \"count\": 5, \"min\": 21.0, \"max\": 41.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 6, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 32, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 56, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 37\n - Network coverage index: 41\n - Temperature control compliance rating: 31\n - Documentation accuracy score: 21\n - Cross-docking efficiency rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.8, \"count\": 5, \"min\": 21.0, \"max\": 41.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 56, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 32, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 61\n - Route optimization index: 67\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 73\n - Damage/loss incident inverse score: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.2, \"count\": 5, \"min\": 61.0, \"max\": 73.0, \"std\": 4.4}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 42, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 61\n - Route optimization index: 67\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 73\n - Damage/loss incident inverse score: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.2, \"count\": 5, \"min\": 61.0, \"max\": 73.0, \"std\": 4.4}\n\nTool: check_external_reference\nOutput: {\"request_id\": 42, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 92, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 61\n - Route optimization index: 67\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 73\n - Damage/loss incident inverse score: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.2, \"count\": 5, \"min\": 61.0, \"max\": 73.0, \"std\": 4.4}\n\nTool: check_external_reference\nOutput: {\"request_id\": 92, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 42, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 61\n - Route optimization index: 67\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 73\n - Damage/loss incident inverse score: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.2, \"count\": 5, \"min\": 61.0, \"max\": 73.0, \"std\": 4.4}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 42, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 92, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 61\n - Route optimization index: 67\n - Fleet utilization rating: 71\n - Customer satisfaction (logistics) score: 73\n - Damage/loss incident inverse score: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.2, \"count\": 5, \"min\": 61.0, \"max\": 73.0, \"std\": 4.4}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 92, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: 50\n - Load factor optimization rating: 41\n - Claims resolution score: 43\n - Last-mile performance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 41.0, \"max\": 56.0, \"std\": 5.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: 50\n - Load factor optimization rating: 41\n - Claims resolution score: 43\n - Last-mile performance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 41.0, \"max\": 56.0, \"std\": 5.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 73, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: 50\n - Load factor optimization rating: 41\n - Claims resolution score: 43\n - Last-mile performance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 41.0, \"max\": 56.0, \"std\": 5.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 73, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: 50\n - Load factor optimization rating: 41\n - Claims resolution score: 43\n - Last-mile performance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 41.0, \"max\": 56.0, \"std\": 5.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 73, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: 50\n - Load factor optimization rating: 41\n - Claims resolution score: 43\n - Last-mile performance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 41.0, \"max\": 56.0, \"std\": 5.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 73, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 66\n - Network coverage index: 60\n - Temperature control compliance rating: 71\n - Documentation accuracy score: 60\n - Cross-docking efficiency rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.8, \"count\": 5, \"min\": 60.0, \"max\": 71.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 66\n - Network coverage index: 60\n - Temperature control compliance rating: 71\n - Documentation accuracy score: 60\n - Cross-docking efficiency rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.8, \"count\": 5, \"min\": 60.0, \"max\": 71.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"request_id\": 35, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 66\n - Network coverage index: 60\n - Temperature control compliance rating: 71\n - Documentation accuracy score: 60\n - Cross-docking efficiency rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.8, \"count\": 5, \"min\": 60.0, \"max\": 71.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 66\n - Network coverage index: 60\n - Temperature control compliance rating: 71\n - Documentation accuracy score: 60\n - Cross-docking efficiency rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.8, \"count\": 5, \"min\": 60.0, \"max\": 71.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 35, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 66\n - Network coverage index: 60\n - Temperature control compliance rating: 71\n - Documentation accuracy score: 60\n - Cross-docking efficiency rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.8, \"count\": 5, \"min\": 60.0, \"max\": 71.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: 43\n - Route optimization index: 50\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 58\n - Damage/loss incident inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.4, \"count\": 5, \"min\": 43.0, \"max\": 58.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: 43\n - Route optimization index: 50\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 58\n - Damage/loss incident inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.4, \"count\": 5, \"min\": 43.0, \"max\": 58.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: 43\n - Route optimization index: 50\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 58\n - Damage/loss incident inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.4, \"count\": 5, \"min\": 43.0, \"max\": 58.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: 43\n - Route optimization index: 50\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 58\n - Damage/loss incident inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.4, \"count\": 5, \"min\": 43.0, \"max\": 58.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: 43\n - Route optimization index: 50\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 58\n - Damage/loss incident inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.4, \"count\": 5, \"min\": 43.0, \"max\": 58.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 54\n - Carrier compliance index: 57\n - Load factor optimization rating: 44\n - Claims resolution score: 54\n - Last-mile performance rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 44.0, \"max\": 66.0, \"std\": 7.04}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 54\n - Carrier compliance index: 57\n - Load factor optimization rating: 44\n - Claims resolution score: 54\n - Last-mile performance rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 44.0, \"max\": 66.0, \"std\": 7.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 54\n - Carrier compliance index: 57\n - Load factor optimization rating: 44\n - Claims resolution score: 54\n - Last-mile performance rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 44.0, \"max\": 66.0, \"std\": 7.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 54\n - Carrier compliance index: 57\n - Load factor optimization rating: 44\n - Claims resolution score: 54\n - Last-mile performance rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 44.0, \"max\": 66.0, \"std\": 7.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 54\n - Carrier compliance index: 57\n - Load factor optimization rating: 44\n - Claims resolution score: 54\n - Last-mile performance rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.0, \"count\": 5, \"min\": 44.0, \"max\": 66.0, \"std\": 7.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 38\n - Network coverage index: 37\n - Temperature control compliance rating: 46\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.4, \"count\": 5, \"min\": 37.0, \"max\": 53.0, \"std\": 5.82}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 38\n - Network coverage index: 37\n - Temperature control compliance rating: 46\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.4, \"count\": 5, \"min\": 37.0, \"max\": 53.0, \"std\": 5.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 38\n - Network coverage index: 37\n - Temperature control compliance rating: 46\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.4, \"count\": 5, \"min\": 37.0, \"max\": 53.0, \"std\": 5.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 38\n - Network coverage index: 37\n - Temperature control compliance rating: 46\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.4, \"count\": 5, \"min\": 37.0, \"max\": 53.0, \"std\": 5.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 25, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 38\n - Network coverage index: 37\n - Temperature control compliance rating: 46\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.4, \"count\": 5, \"min\": 37.0, \"max\": 53.0, \"std\": 5.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 48\n - Route optimization index: 52\n - Fleet utilization rating: 43\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.35}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 48\n - Route optimization index: 52\n - Fleet utilization rating: 43\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 1, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 48\n - Route optimization index: 52\n - Fleet utilization rating: 43\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 48\n - Route optimization index: 52\n - Fleet utilization rating: 43\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 1, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 48\n - Route optimization index: 52\n - Fleet utilization rating: 43\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 48\n - Carrier compliance index: 56\n - Load factor optimization rating: 71\n - Claims resolution score: 50\n - Last-mile performance rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 14, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 48\n - Carrier compliance index: 56\n - Load factor optimization rating: 71\n - Claims resolution score: 50\n - Last-mile performance rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"request_id\": 14, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 94, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 48\n - Carrier compliance index: 56\n - Load factor optimization rating: 71\n - Claims resolution score: 50\n - Last-mile performance rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"request_id\": 94, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 14, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 48\n - Carrier compliance index: 56\n - Load factor optimization rating: 71\n - Claims resolution score: 50\n - Last-mile performance rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 14, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 94, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 48\n - Carrier compliance index: 56\n - Load factor optimization rating: 71\n - Claims resolution score: 50\n - Last-mile performance rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.8, \"count\": 5, \"min\": 48.0, \"max\": 71.0, \"std\": 8.57}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 94, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: 40\n - Network coverage index: 54\n - Temperature control compliance rating: 75\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.4, \"count\": 5, \"min\": 40.0, \"max\": 75.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: 40\n - Network coverage index: 54\n - Temperature control compliance rating: 75\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.4, \"count\": 5, \"min\": 40.0, \"max\": 75.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: 40\n - Network coverage index: 54\n - Temperature control compliance rating: 75\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.4, \"count\": 5, \"min\": 40.0, \"max\": 75.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 93, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: 40\n - Network coverage index: 54\n - Temperature control compliance rating: 75\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.4, \"count\": 5, \"min\": 40.0, \"max\": 75.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: 40\n - Network coverage index: 54\n - Temperature control compliance rating: 75\n - Documentation accuracy score: 43\n - Cross-docking efficiency rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.4, \"count\": 5, \"min\": 40.0, \"max\": 75.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 93, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: 23\n - Route optimization index: 34\n - Fleet utilization rating: 31\n - Customer satisfaction (logistics) score: 23\n - Damage/loss incident inverse score: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 23.0, \"max\": 34.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: 23\n - Route optimization index: 34\n - Fleet utilization rating: 31\n - Customer satisfaction (logistics) score: 23\n - Damage/loss incident inverse score: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 23.0, \"max\": 34.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: 23\n - Route optimization index: 34\n - Fleet utilization rating: 31\n - Customer satisfaction (logistics) score: 23\n - Damage/loss incident inverse score: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 23.0, \"max\": 34.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: 23\n - Route optimization index: 34\n - Fleet utilization rating: 31\n - Customer satisfaction (logistics) score: 23\n - Damage/loss incident inverse score: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 23.0, \"max\": 34.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: 23\n - Route optimization index: 34\n - Fleet utilization rating: 31\n - Customer satisfaction (logistics) score: 23\n - Damage/loss incident inverse score: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 23.0, \"max\": 34.0, \"std\": 4.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 64\n - Carrier compliance index: 55\n - Load factor optimization rating: 57\n - Claims resolution score: 52\n - Last-mile performance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.8, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 64\n - Carrier compliance index: 55\n - Load factor optimization rating: 57\n - Claims resolution score: 52\n - Last-mile performance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.8, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 96, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 64\n - Carrier compliance index: 55\n - Load factor optimization rating: 57\n - Claims resolution score: 52\n - Last-mile performance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.8, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"request_id\": 96, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 64\n - Carrier compliance index: 55\n - Load factor optimization rating: 57\n - Claims resolution score: 52\n - Last-mile performance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.8, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 96, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: 64\n - Carrier compliance index: 55\n - Load factor optimization rating: 57\n - Claims resolution score: 52\n - Last-mile performance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.8, \"count\": 5, \"min\": 52.0, \"max\": 64.0, \"std\": 4.26}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 96, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 34\n - Network coverage index: 49\n - Temperature control compliance rating: 50\n - Documentation accuracy score: 42\n - Cross-docking efficiency rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.6, \"count\": 5, \"min\": 34.0, \"max\": 50.0, \"std\": 5.75}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 6, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 34\n - Network coverage index: 49\n - Temperature control compliance rating: 50\n - Documentation accuracy score: 42\n - Cross-docking efficiency rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.6, \"count\": 5, \"min\": 34.0, \"max\": 50.0, \"std\": 5.75}\n\nTool: check_external_reference\nOutput: {\"request_id\": 6, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 86, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 34\n - Network coverage index: 49\n - Temperature control compliance rating: 50\n - Documentation accuracy score: 42\n - Cross-docking efficiency rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.6, \"count\": 5, \"min\": 34.0, \"max\": 50.0, \"std\": 5.75}\n\nTool: check_external_reference\nOutput: {\"request_id\": 86, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 6, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 34\n - Network coverage index: 49\n - Temperature control compliance rating: 50\n - Documentation accuracy score: 42\n - Cross-docking efficiency rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.6, \"count\": 5, \"min\": 34.0, \"max\": 50.0, \"std\": 5.75}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 6, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 86, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 34\n - Network coverage index: 49\n - Temperature control compliance rating: 50\n - Documentation accuracy score: 42\n - Cross-docking efficiency rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.6, \"count\": 5, \"min\": 34.0, \"max\": 50.0, \"std\": 5.75}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 86, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 57\n - Route optimization index: 65\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 56\n - Damage/loss incident inverse score: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.4, \"count\": 5, \"min\": 51.0, \"max\": 65.0, \"std\": 4.8}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 57\n - Route optimization index: 65\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 56\n - Damage/loss incident inverse score: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.4, \"count\": 5, \"min\": 51.0, \"max\": 65.0, \"std\": 4.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 95, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 57\n - Route optimization index: 65\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 56\n - Damage/loss incident inverse score: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.4, \"count\": 5, \"min\": 51.0, \"max\": 65.0, \"std\": 4.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 95, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 57\n - Route optimization index: 65\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 56\n - Damage/loss incident inverse score: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.4, \"count\": 5, \"min\": 51.0, \"max\": 65.0, \"std\": 4.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 95, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 57\n - Route optimization index: 65\n - Fleet utilization rating: 53\n - Customer satisfaction (logistics) score: 56\n - Damage/loss incident inverse score: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.4, \"count\": 5, \"min\": 51.0, \"max\": 65.0, \"std\": 4.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 95, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 13\n - Carrier compliance index: 31\n - Load factor optimization rating: 29\n - Claims resolution score: 34\n - Last-mile performance rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.8, \"count\": 5, \"min\": 13.0, \"max\": 42.0, \"std\": 9.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 13\n - Carrier compliance index: 31\n - Load factor optimization rating: 29\n - Claims resolution score: 34\n - Last-mile performance rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.8, \"count\": 5, \"min\": 13.0, \"max\": 42.0, \"std\": 9.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 13\n - Carrier compliance index: 31\n - Load factor optimization rating: 29\n - Claims resolution score: 34\n - Last-mile performance rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.8, \"count\": 5, \"min\": 13.0, \"max\": 42.0, \"std\": 9.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 13\n - Carrier compliance index: 31\n - Load factor optimization rating: 29\n - Claims resolution score: 34\n - Last-mile performance rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.8, \"count\": 5, \"min\": 13.0, \"max\": 42.0, \"std\": 9.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 13\n - Carrier compliance index: 31\n - Load factor optimization rating: 29\n - Claims resolution score: 34\n - Last-mile performance rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.8, \"count\": 5, \"min\": 13.0, \"max\": 42.0, \"std\": 9.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: 66\n - Temperature control compliance rating: 58\n - Documentation accuracy score: 62\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: 66\n - Temperature control compliance rating: 58\n - Documentation accuracy score: 62\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 98, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: 66\n - Temperature control compliance rating: 58\n - Documentation accuracy score: 62\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"request_id\": 98, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: 66\n - Temperature control compliance rating: 58\n - Documentation accuracy score: 62\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 98, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: 66\n - Temperature control compliance rating: 58\n - Documentation accuracy score: 62\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 98, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 39\n - Fleet utilization rating: 30\n - Customer satisfaction (logistics) score: 36\n - Damage/loss incident inverse score: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.2, \"count\": 5, \"min\": 30.0, \"max\": 39.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 39\n - Fleet utilization rating: 30\n - Customer satisfaction (logistics) score: 36\n - Damage/loss incident inverse score: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.2, \"count\": 5, \"min\": 30.0, \"max\": 39.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 39\n - Fleet utilization rating: 30\n - Customer satisfaction (logistics) score: 36\n - Damage/loss incident inverse score: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.2, \"count\": 5, \"min\": 30.0, \"max\": 39.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 39\n - Fleet utilization rating: 30\n - Customer satisfaction (logistics) score: 36\n - Damage/loss incident inverse score: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.2, \"count\": 5, \"min\": 30.0, \"max\": 39.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-e-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "easy", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 39\n - Fleet utilization rating: 30\n - Customer satisfaction (logistics) score: 36\n - Damage/loss incident inverse score: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.2, \"count\": 5, \"min\": 30.0, \"max\": 39.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 43\n - Carrier compliance index: 57\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 42, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 43\n - Carrier compliance index: 57\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 42, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 43\n - Carrier compliance index: 57\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 42, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 43\n - Carrier compliance index: 57\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 42, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: 43\n - Carrier compliance index: 57\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 75\n - Network coverage index: 31\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 31.0, \"max\": 76.0, \"std\": 20.98}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 75\n - Network coverage index: 31\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 31.0, \"max\": 76.0, \"std\": 20.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 75\n - Network coverage index: 31\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 31.0, \"max\": 76.0, \"std\": 20.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 75\n - Network coverage index: 31\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 31.0, \"max\": 76.0, \"std\": 20.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 75\n - Network coverage index: 31\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 76\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.67, \"count\": 3, \"min\": 31.0, \"max\": 76.0, \"std\": 20.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: [data not available]\n - Fleet utilization rating: 70\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.33, \"count\": 3, \"min\": 52.0, \"max\": 70.0, \"std\": 7.72}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 51, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: [data not available]\n - Fleet utilization rating: 70\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.33, \"count\": 3, \"min\": 52.0, \"max\": 70.0, \"std\": 7.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 51, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: [data not available]\n - Fleet utilization rating: 70\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.33, \"count\": 3, \"min\": 52.0, \"max\": 70.0, \"std\": 7.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 51, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: [data not available]\n - Fleet utilization rating: 70\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.33, \"count\": 3, \"min\": 52.0, \"max\": 70.0, \"std\": 7.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 51, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: [data not available]\n - Fleet utilization rating: 70\n - Customer satisfaction (logistics) score: 52\n - Damage/loss incident inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.33, \"count\": 3, \"min\": 52.0, \"max\": 70.0, \"std\": 7.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 28\n - Load factor optimization rating: 60\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 28\n - Load factor optimization rating: 60\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 53, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 83, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 28\n - Load factor optimization rating: 60\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 83, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 28\n - Load factor optimization rating: 60\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 53, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 83, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 28\n - Load factor optimization rating: 60\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 28.0, \"max\": 60.0, \"std\": 13.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 83, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 43\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 36.0, \"max\": 72.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 43\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 36.0, \"max\": 72.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 43\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 36.0, \"max\": 72.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 43\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 36.0, \"max\": 72.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 43\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 36\n - Cross-docking efficiency rating: 72\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 36.0, \"max\": 72.0, \"std\": 15.58}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 41\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 37\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 37.0, \"max\": 83.0, \"std\": 20.81}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 41\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 37\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 37.0, \"max\": 83.0, \"std\": 20.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 41\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 37\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 37.0, \"max\": 83.0, \"std\": 20.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 41\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 37\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 37.0, \"max\": 83.0, \"std\": 20.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 41\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 37\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 37.0, \"max\": 83.0, \"std\": 20.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 43\n - Claims resolution score: 57\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 43.0, \"max\": 64.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 43\n - Claims resolution score: 57\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 43.0, \"max\": 64.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 60, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 43\n - Claims resolution score: 57\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 43.0, \"max\": 64.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 60, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 43\n - Claims resolution score: 57\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 43.0, \"max\": 64.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 60, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 43\n - Claims resolution score: 57\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 43.0, \"max\": 64.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 60, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 29\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 56\n - Cross-docking efficiency rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 29.0, \"max\": 59.0, \"std\": 13.49}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 29\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 56\n - Cross-docking efficiency rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 29.0, \"max\": 59.0, \"std\": 13.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 29\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 56\n - Cross-docking efficiency rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 29.0, \"max\": 59.0, \"std\": 13.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 29\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 56\n - Cross-docking efficiency rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 29.0, \"max\": 59.0, \"std\": 13.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Transit time consistency score: 29\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 56\n - Cross-docking efficiency rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 29.0, \"max\": 59.0, \"std\": 13.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 32\n - Route optimization index: [data not available]\n - Fleet utilization rating: 56\n - Customer satisfaction (logistics) score: 30\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.33, \"count\": 3, \"min\": 30.0, \"max\": 56.0, \"std\": 11.81}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 32\n - Route optimization index: [data not available]\n - Fleet utilization rating: 56\n - Customer satisfaction (logistics) score: 30\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.33, \"count\": 3, \"min\": 30.0, \"max\": 56.0, \"std\": 11.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 32\n - Route optimization index: [data not available]\n - Fleet utilization rating: 56\n - Customer satisfaction (logistics) score: 30\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.33, \"count\": 3, \"min\": 30.0, \"max\": 56.0, \"std\": 11.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 47, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 32\n - Route optimization index: [data not available]\n - Fleet utilization rating: 56\n - Customer satisfaction (logistics) score: 30\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.33, \"count\": 3, \"min\": 30.0, \"max\": 56.0, \"std\": 11.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - On-time delivery rate score: 32\n - Route optimization index: [data not available]\n - Fleet utilization rating: 56\n - Customer satisfaction (logistics) score: 30\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.33, \"count\": 3, \"min\": 30.0, \"max\": 56.0, \"std\": 11.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 47, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 47\n - Claims resolution score: 29\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 29.0, \"max\": 64.0, \"std\": 14.29}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 39, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 47\n - Claims resolution score: 29\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 29.0, \"max\": 64.0, \"std\": 14.29}\n\nTool: check_external_reference\nOutput: {\"request_id\": 39, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 47\n - Claims resolution score: 29\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 29.0, \"max\": 64.0, \"std\": 14.29}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 39, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 47\n - Claims resolution score: 29\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 29.0, \"max\": 64.0, \"std\": 14.29}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 39, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 15, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 47\n - Claims resolution score: 29\n - Last-mile performance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 29.0, \"max\": 64.0, \"std\": 14.29}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 28\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 61\n - Cross-docking efficiency rating: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 25.0, \"max\": 61.0, \"std\": 16.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 14, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 28\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 61\n - Cross-docking efficiency rating: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 25.0, \"max\": 61.0, \"std\": 16.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 14, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 64, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 28\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 61\n - Cross-docking efficiency rating: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 25.0, \"max\": 61.0, \"std\": 16.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 64, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 14, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 28\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 61\n - Cross-docking efficiency rating: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 25.0, \"max\": 61.0, \"std\": 16.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 14, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 64, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Transit time consistency score: 28\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 61\n - Cross-docking efficiency rating: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.0, \"count\": 3, \"min\": 25.0, \"max\": 61.0, \"std\": 16.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 64, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 40\n - Fleet utilization rating: 49\n - Customer satisfaction (logistics) score: 78\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 78.0, \"std\": 16.21}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 40\n - Fleet utilization rating: 49\n - Customer satisfaction (logistics) score: 78\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 78.0, \"std\": 16.21}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 40\n - Fleet utilization rating: 49\n - Customer satisfaction (logistics) score: 78\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 78.0, \"std\": 16.21}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 40\n - Fleet utilization rating: 49\n - Customer satisfaction (logistics) score: 78\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 78.0, \"std\": 16.21}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 40\n - Fleet utilization rating: 49\n - Customer satisfaction (logistics) score: 78\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 78.0, \"std\": 16.21}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 62\n - Last-mile performance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 38.0, \"max\": 62.0, \"std\": 9.9}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 62\n - Last-mile performance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 38.0, \"max\": 62.0, \"std\": 9.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 62\n - Last-mile performance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 38.0, \"max\": 62.0, \"std\": 9.9}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 62\n - Last-mile performance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 38.0, \"max\": 62.0, \"std\": 9.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 62\n - Last-mile performance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 38.0, \"max\": 62.0, \"std\": 9.9}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 73\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 30.0, \"max\": 73.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 36, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 73\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 30.0, \"max\": 73.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 36, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 86, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 73\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 30.0, \"max\": 73.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 86, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 36, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 73\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 30.0, \"max\": 73.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 36, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 86, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Transit time consistency score: 59\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 73\n - Documentation accuracy score: [data not available]\n - Cross-docking efficiency rating: 30\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 30.0, \"max\": 73.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 86, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 71\n - Route optimization index: [data not available]\n - Fleet utilization rating: 32\n - Customer satisfaction (logistics) score: 40\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 32.0, \"max\": 71.0, \"std\": 16.82}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 71\n - Route optimization index: [data not available]\n - Fleet utilization rating: 32\n - Customer satisfaction (logistics) score: 40\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 32.0, \"max\": 71.0, \"std\": 16.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 71\n - Route optimization index: [data not available]\n - Fleet utilization rating: 32\n - Customer satisfaction (logistics) score: 40\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 32.0, \"max\": 71.0, \"std\": 16.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 71\n - Route optimization index: [data not available]\n - Fleet utilization rating: 32\n - Customer satisfaction (logistics) score: 40\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 32.0, \"max\": 71.0, \"std\": 16.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - On-time delivery rate score: 71\n - Route optimization index: [data not available]\n - Fleet utilization rating: 32\n - Customer satisfaction (logistics) score: 40\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 32.0, \"max\": 71.0, \"std\": 16.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 61\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 34.0, \"max\": 61.0, \"std\": 11.73}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 61\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 34.0, \"max\": 61.0, \"std\": 11.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 61\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 34.0, \"max\": 61.0, \"std\": 11.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 61\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 34.0, \"max\": 61.0, \"std\": 11.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - Shipment tracking accuracy score: 61\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 56\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 34.0, \"max\": 61.0, \"std\": 11.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 57\n - Documentation accuracy score: 0\n - Cross-docking efficiency rating: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 0.0, \"max\": 68.0, \"std\": 29.8}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 57\n - Documentation accuracy score: 0\n - Cross-docking efficiency rating: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 0.0, \"max\": 68.0, \"std\": 29.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 57\n - Documentation accuracy score: 0\n - Cross-docking efficiency rating: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 0.0, \"max\": 68.0, \"std\": 29.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 57\n - Documentation accuracy score: 0\n - Cross-docking efficiency rating: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 0.0, \"max\": 68.0, \"std\": 29.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 57\n - Documentation accuracy score: 0\n - Cross-docking efficiency rating: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 0.0, \"max\": 68.0, \"std\": 29.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 56\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 49\n - Damage/loss incident inverse score: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 35.0, \"max\": 56.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 56\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 49\n - Damage/loss incident inverse score: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 35.0, \"max\": 56.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 56\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 49\n - Damage/loss incident inverse score: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 35.0, \"max\": 56.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 56\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 49\n - Damage/loss incident inverse score: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 35.0, \"max\": 56.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 56\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 49\n - Damage/loss incident inverse score: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.67, \"count\": 3, \"min\": 35.0, \"max\": 56.0, \"std\": 8.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 39\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 52\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 34.0, \"max\": 52.0, \"std\": 7.59}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 39, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 39\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 52\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 34.0, \"max\": 52.0, \"std\": 7.59}\n\nTool: check_external_reference\nOutput: {\"request_id\": 39, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 89, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 39\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 52\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 34.0, \"max\": 52.0, \"std\": 7.59}\n\nTool: check_external_reference\nOutput: {\"request_id\": 89, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 39, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 39\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 52\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 34.0, \"max\": 52.0, \"std\": 7.59}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 39, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 89, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 39\n - Load factor optimization rating: [data not available]\n - Claims resolution score: 52\n - Last-mile performance rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.67, \"count\": 3, \"min\": 34.0, \"max\": 52.0, \"std\": 7.59}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 89, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 48\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 23\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.67, \"count\": 3, \"min\": 23.0, \"max\": 51.0, \"std\": 12.55}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 48\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 23\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.67, \"count\": 3, \"min\": 23.0, \"max\": 51.0, \"std\": 12.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 48\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 23\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.67, \"count\": 3, \"min\": 23.0, \"max\": 51.0, \"std\": 12.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 48\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 23\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.67, \"count\": 3, \"min\": 23.0, \"max\": 51.0, \"std\": 12.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 25, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Transit time consistency score: 48\n - Network coverage index: [data not available]\n - Temperature control compliance rating: [data not available]\n - Documentation accuracy score: 23\n - Cross-docking efficiency rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.67, \"count\": 3, \"min\": 23.0, \"max\": 51.0, \"std\": 12.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 52\n - Route optimization index: [data not available]\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 48\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 3, \"min\": 48.0, \"max\": 83.0, \"std\": 15.64}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 2, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 52\n - Route optimization index: [data not available]\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 48\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 3, \"min\": 48.0, \"max\": 83.0, \"std\": 15.64}\n\nTool: check_external_reference\nOutput: {\"request_id\": 2, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 52\n - Route optimization index: [data not available]\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 48\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 3, \"min\": 48.0, \"max\": 83.0, \"std\": 15.64}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 2, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 52\n - Route optimization index: [data not available]\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 48\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 3, \"min\": 48.0, \"max\": 83.0, \"std\": 15.64}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 2, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - On-time delivery rate score: 52\n - Route optimization index: [data not available]\n - Fleet utilization rating: 83\n - Customer satisfaction (logistics) score: 48\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 3, \"min\": 48.0, \"max\": 83.0, \"std\": 15.64}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 85\n - Load factor optimization rating: 50\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 50.0, \"max\": 85.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 85\n - Load factor optimization rating: 50\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 50.0, \"max\": 85.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 85\n - Load factor optimization rating: 50\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 50.0, \"max\": 85.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 85\n - Load factor optimization rating: 50\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 50.0, \"max\": 85.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: 85\n - Load factor optimization rating: 50\n - Claims resolution score: [data not available]\n - Last-mile performance rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 50.0, \"max\": 85.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 3\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 30\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.67, \"count\": 3, \"min\": 3.0, \"max\": 53.0, \"std\": 20.43}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 3\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 30\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.67, \"count\": 3, \"min\": 3.0, \"max\": 53.0, \"std\": 20.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 3\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 30\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.67, \"count\": 3, \"min\": 3.0, \"max\": 53.0, \"std\": 20.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 3\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 30\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.67, \"count\": 3, \"min\": 3.0, \"max\": 53.0, \"std\": 20.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA parcel delivery network is reviewing regional reliability performance. The following scores were collected from independent monitors.\n\nEvidence:\n - Transit time consistency score: 3\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 53\n - Documentation accuracy score: 30\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.67, \"count\": 3, \"min\": 3.0, \"max\": 53.0, \"std\": 20.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 16\n - Route optimization index: 51\n - Fleet utilization rating: 77\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 16.0, \"max\": 77.0, \"std\": 24.99}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 16\n - Route optimization index: 51\n - Fleet utilization rating: 77\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 16.0, \"max\": 77.0, \"std\": 24.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 16\n - Route optimization index: 51\n - Fleet utilization rating: 77\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 16.0, \"max\": 77.0, \"std\": 24.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 16\n - Route optimization index: 51\n - Fleet utilization rating: 77\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 16.0, \"max\": 77.0, \"std\": 24.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA rail freight operator is evaluating its intermodal logistics reliability. Five audit scores are available.\n\nEvidence:\n - On-time delivery rate score: 16\n - Route optimization index: 51\n - Fleet utilization rating: 77\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.0, \"count\": 3, \"min\": 16.0, \"max\": 77.0, \"std\": 24.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 70\n - Last-mile performance rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 34.0, \"max\": 70.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 70\n - Last-mile performance rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 34.0, \"max\": 70.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 70\n - Last-mile performance rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 34.0, \"max\": 70.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 70\n - Last-mile performance rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 34.0, \"max\": 70.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn international courier service is assessing cross-border delivery reliability. Performance ratings are summarized below.\n\nEvidence:\n - Shipment tracking accuracy score: [data not available]\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 34\n - Claims resolution score: 70\n - Last-mile performance rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 34.0, \"max\": 70.0, \"std\": 14.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 62\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 29\n - Documentation accuracy score: 95\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 29.0, \"max\": 95.0, \"std\": 26.94}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 62\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 29\n - Documentation accuracy score: 95\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 29.0, \"max\": 95.0, \"std\": 26.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 62\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 29\n - Documentation accuracy score: 95\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 29.0, \"max\": 95.0, \"std\": 26.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 62\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 29\n - Documentation accuracy score: 95\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 29.0, \"max\": 95.0, \"std\": 26.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA shipping company is evaluating its overall logistics reliability. Five operational metrics (scored 0–100) are summarized below.\n\nEvidence:\n - Transit time consistency score: 62\n - Network coverage index: [data not available]\n - Temperature control compliance rating: 29\n - Documentation accuracy score: 95\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 29.0, \"max\": 95.0, \"std\": 26.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 51\n - Fleet utilization rating: 69\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 27.0, \"max\": 69.0, \"std\": 17.2}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 51\n - Fleet utilization rating: 69\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 27.0, \"max\": 69.0, \"std\": 17.2}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 51\n - Fleet utilization rating: 69\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 27.0, \"max\": 69.0, \"std\": 17.2}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 51\n - Fleet utilization rating: 69\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 27.0, \"max\": 69.0, \"std\": 17.2}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA last-mile delivery startup is assessing route reliability. Independent performance metrics are as follows.\n\nEvidence:\n - On-time delivery rate score: [data not available]\n - Route optimization index: 51\n - Fleet utilization rating: 69\n - Customer satisfaction (logistics) score: [data not available]\n - Damage/loss incident inverse score: 27\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 27.0, \"max\": 69.0, \"std\": 17.2}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the performance data above, what is your best estimate for the reliability score (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 88\n - Claims resolution score: 18\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 18.0, \"max\": 88.0, \"std\": 28.72}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 88\n - Claims resolution score: 18\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 18.0, \"max\": 88.0, \"std\": 28.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 88\n - Claims resolution score: 18\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 18.0, \"max\": 88.0, \"std\": 28.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 88\n - Claims resolution score: 18\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 18.0, \"max\": 88.0, \"std\": 28.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn airline cargo division is rating its logistics performance. Five key performance indicators have been scored.\n\nEvidence:\n - Shipment tracking accuracy score: 47\n - Carrier compliance index: [data not available]\n - Load factor optimization rating: 88\n - Claims resolution score: 18\n - Last-mile performance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 18.0, \"max\": 88.0, \"std\": 28.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite logistics reliability score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 55\n - Temperature control compliance rating: 69\n - Documentation accuracy score: 53\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 53.0, \"max\": 69.0, \"std\": 7.12}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 55\n - Temperature control compliance rating: 69\n - Documentation accuracy score: 53\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 53.0, \"max\": 69.0, \"std\": 7.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 55\n - Temperature control compliance rating: 69\n - Documentation accuracy score: 53\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 53.0, \"max\": 69.0, \"std\": 7.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 88, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 55\n - Temperature control compliance rating: 69\n - Documentation accuracy score: 53\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 53.0, \"max\": 69.0, \"std\": 7.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA freight broker is evaluating carrier reliability. The following rating data is available from five audit sources.\n\nEvidence:\n - Transit time consistency score: [data not available]\n - Network coverage index: 55\n - Temperature control compliance rating: 69\n - Documentation accuracy score: 53\n - Cross-docking efficiency rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 59.0, \"count\": 3, \"min\": 53.0, \"max\": 69.0, \"std\": 7.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 88, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall reliability index (0–100) do you estimate based on these performance data?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 57\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 15\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 15.0, \"max\": 57.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 3, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 57\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 15\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 15.0, \"max\": 57.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 3, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 83, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 57\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 15\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 15.0, \"max\": 57.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 83, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 3, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 57\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 15\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 15.0, \"max\": 57.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 3, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-transportation_logistics-h-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 83, "offset": 40, "difficulty": "hard", "domain": "transportation_logistics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA cold-chain logistics provider is assessing delivery reliability for perishable goods. Five indicators are summarized below.\n\nEvidence:\n - On-time delivery rate score: 37\n - Route optimization index: 57\n - Fleet utilization rating: [data not available]\n - Customer satisfaction (logistics) score: 15\n - Damage/loss incident inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 15.0, \"max\": 57.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 83, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these metrics, estimate the overall logistics reliability index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 48\n - Recycling rate index: 35\n - Emissions intensity rating: 40\n - Resource recovery score: 36\n - Environmental management system rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.6, \"count\": 5, \"min\": 35.0, \"max\": 48.0, \"std\": 4.88}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 48\n - Recycling rate index: 35\n - Emissions intensity rating: 40\n - Resource recovery score: 36\n - Environmental management system rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.6, \"count\": 5, \"min\": 35.0, \"max\": 48.0, \"std\": 4.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 48\n - Recycling rate index: 35\n - Emissions intensity rating: 40\n - Resource recovery score: 36\n - Environmental management system rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.6, \"count\": 5, \"min\": 35.0, \"max\": 48.0, \"std\": 4.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 48\n - Recycling rate index: 35\n - Emissions intensity rating: 40\n - Resource recovery score: 36\n - Environmental management system rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.6, \"count\": 5, \"min\": 35.0, \"max\": 48.0, \"std\": 4.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 48\n - Recycling rate index: 35\n - Emissions intensity rating: 40\n - Resource recovery score: 36\n - Environmental management system rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.6, \"count\": 5, \"min\": 35.0, \"max\": 48.0, \"std\": 4.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 44\n - Packaging efficiency rating: 23\n - Supply chain carbon score: 35\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 5, \"min\": 23.0, \"max\": 46.0, \"std\": 8.12}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 44\n - Packaging efficiency rating: 23\n - Supply chain carbon score: 35\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 5, \"min\": 23.0, \"max\": 46.0, \"std\": 8.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 51, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 44\n - Packaging efficiency rating: 23\n - Supply chain carbon score: 35\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 5, \"min\": 23.0, \"max\": 46.0, \"std\": 8.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 51, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 44\n - Packaging efficiency rating: 23\n - Supply chain carbon score: 35\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 5, \"min\": 23.0, \"max\": 46.0, \"std\": 8.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 51, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 44\n - Packaging efficiency rating: 23\n - Supply chain carbon score: 35\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.0, \"count\": 5, \"min\": 23.0, \"max\": 46.0, \"std\": 8.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 51, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 52\n - Water usage optimization index: 46\n - Material waste reduction rating: 48\n - Carbon footprint benchmark score: 54\n - Sustainability compliance rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 52\n - Water usage optimization index: 46\n - Material waste reduction rating: 48\n - Carbon footprint benchmark score: 54\n - Sustainability compliance rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 61, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 52\n - Water usage optimization index: 46\n - Material waste reduction rating: 48\n - Carbon footprint benchmark score: 54\n - Sustainability compliance rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 61, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 52\n - Water usage optimization index: 46\n - Material waste reduction rating: 48\n - Carbon footprint benchmark score: 54\n - Sustainability compliance rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 61, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 52\n - Water usage optimization index: 46\n - Material waste reduction rating: 48\n - Carbon footprint benchmark score: 54\n - Sustainability compliance rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.0, \"count\": 5, \"min\": 46.0, \"max\": 65.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 61, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 35\n - Recycling rate index: 43\n - Emissions intensity rating: 52\n - Resource recovery score: 39\n - Environmental management system rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.8, \"count\": 5, \"min\": 35.0, \"max\": 52.0, \"std\": 6.43}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 35\n - Recycling rate index: 43\n - Emissions intensity rating: 52\n - Resource recovery score: 39\n - Environmental management system rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.8, \"count\": 5, \"min\": 35.0, \"max\": 52.0, \"std\": 6.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 35\n - Recycling rate index: 43\n - Emissions intensity rating: 52\n - Resource recovery score: 39\n - Environmental management system rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.8, \"count\": 5, \"min\": 35.0, \"max\": 52.0, \"std\": 6.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 62, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 35\n - Recycling rate index: 43\n - Emissions intensity rating: 52\n - Resource recovery score: 39\n - Environmental management system rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.8, \"count\": 5, \"min\": 35.0, \"max\": 52.0, \"std\": 6.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 35\n - Recycling rate index: 43\n - Emissions intensity rating: 52\n - Resource recovery score: 39\n - Environmental management system rating: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.8, \"count\": 5, \"min\": 35.0, \"max\": 52.0, \"std\": 6.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 62, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 36\n - Water reclamation index: 20\n - Packaging efficiency rating: 37\n - Supply chain carbon score: 15\n - Biodiversity impact rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.8, \"count\": 5, \"min\": 15.0, \"max\": 37.0, \"std\": 9.37}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 36\n - Water reclamation index: 20\n - Packaging efficiency rating: 37\n - Supply chain carbon score: 15\n - Biodiversity impact rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.8, \"count\": 5, \"min\": 15.0, \"max\": 37.0, \"std\": 9.37}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 36\n - Water reclamation index: 20\n - Packaging efficiency rating: 37\n - Supply chain carbon score: 15\n - Biodiversity impact rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.8, \"count\": 5, \"min\": 15.0, \"max\": 37.0, \"std\": 9.37}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 36\n - Water reclamation index: 20\n - Packaging efficiency rating: 37\n - Supply chain carbon score: 15\n - Biodiversity impact rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.8, \"count\": 5, \"min\": 15.0, \"max\": 37.0, \"std\": 9.37}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 36\n - Water reclamation index: 20\n - Packaging efficiency rating: 37\n - Supply chain carbon score: 15\n - Biodiversity impact rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.8, \"count\": 5, \"min\": 15.0, \"max\": 37.0, \"std\": 9.37}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 45\n - Material waste reduction rating: 43\n - Carbon footprint benchmark score: 46\n - Sustainability compliance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.01}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 45\n - Material waste reduction rating: 43\n - Carbon footprint benchmark score: 46\n - Sustainability compliance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 61, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 45\n - Material waste reduction rating: 43\n - Carbon footprint benchmark score: 46\n - Sustainability compliance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 61, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 45\n - Material waste reduction rating: 43\n - Carbon footprint benchmark score: 46\n - Sustainability compliance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 61, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 45\n - Material waste reduction rating: 43\n - Carbon footprint benchmark score: 46\n - Sustainability compliance rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.4, \"count\": 5, \"min\": 43.0, \"max\": 52.0, \"std\": 3.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 61, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 67\n - Recycling rate index: 64\n - Emissions intensity rating: 60\n - Resource recovery score: 57\n - Environmental management system rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.95}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 67\n - Recycling rate index: 64\n - Emissions intensity rating: 60\n - Resource recovery score: 57\n - Environmental management system rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.95}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 76, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 67\n - Recycling rate index: 64\n - Emissions intensity rating: 60\n - Resource recovery score: 57\n - Environmental management system rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.95}\n\nTool: check_external_reference\nOutput: {\"request_id\": 76, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 67\n - Recycling rate index: 64\n - Emissions intensity rating: 60\n - Resource recovery score: 57\n - Environmental management system rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.95}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 76, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 67\n - Recycling rate index: 64\n - Emissions intensity rating: 60\n - Resource recovery score: 57\n - Environmental management system rating: 67\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.95}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 76, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: 38\n - Water reclamation index: 52\n - Packaging efficiency rating: 59\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 38.0, \"max\": 69.0, \"std\": 11.44}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 37, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: 38\n - Water reclamation index: 52\n - Packaging efficiency rating: 59\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 38.0, \"max\": 69.0, \"std\": 11.44}\n\nTool: check_external_reference\nOutput: {\"request_id\": 37, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: 38\n - Water reclamation index: 52\n - Packaging efficiency rating: 59\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 38.0, \"max\": 69.0, \"std\": 11.44}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 37, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: 38\n - Water reclamation index: 52\n - Packaging efficiency rating: 59\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 38.0, \"max\": 69.0, \"std\": 11.44}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 37, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: 38\n - Water reclamation index: 52\n - Packaging efficiency rating: 59\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 38.0, \"max\": 69.0, \"std\": 11.44}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 65\n - Water usage optimization index: 45\n - Material waste reduction rating: 51\n - Carbon footprint benchmark score: 51\n - Sustainability compliance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 45.0, \"max\": 65.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 34, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 65\n - Water usage optimization index: 45\n - Material waste reduction rating: 51\n - Carbon footprint benchmark score: 51\n - Sustainability compliance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 45.0, \"max\": 65.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 34, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 64, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 65\n - Water usage optimization index: 45\n - Material waste reduction rating: 51\n - Carbon footprint benchmark score: 51\n - Sustainability compliance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 45.0, \"max\": 65.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 64, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 34, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 65\n - Water usage optimization index: 45\n - Material waste reduction rating: 51\n - Carbon footprint benchmark score: 51\n - Sustainability compliance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 45.0, \"max\": 65.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 34, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 64, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 65\n - Water usage optimization index: 45\n - Material waste reduction rating: 51\n - Carbon footprint benchmark score: 51\n - Sustainability compliance rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 45.0, \"max\": 65.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 64, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 44\n - Recycling rate index: 43\n - Emissions intensity rating: 40\n - Resource recovery score: 48\n - Environmental management system rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.8, \"count\": 5, \"min\": 40.0, \"max\": 49.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 44\n - Recycling rate index: 43\n - Emissions intensity rating: 40\n - Resource recovery score: 48\n - Environmental management system rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.8, \"count\": 5, \"min\": 40.0, \"max\": 49.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 57, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 44\n - Recycling rate index: 43\n - Emissions intensity rating: 40\n - Resource recovery score: 48\n - Environmental management system rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.8, \"count\": 5, \"min\": 40.0, \"max\": 49.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 57, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 44\n - Recycling rate index: 43\n - Emissions intensity rating: 40\n - Resource recovery score: 48\n - Environmental management system rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.8, \"count\": 5, \"min\": 40.0, \"max\": 49.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 57, "offset": 15, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 44\n - Recycling rate index: 43\n - Emissions intensity rating: 40\n - Resource recovery score: 48\n - Environmental management system rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.8, \"count\": 5, \"min\": 40.0, \"max\": 49.0, \"std\": 3.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 57, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 32\n - Water reclamation index: 44\n - Packaging efficiency rating: 39\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.2, \"count\": 5, \"min\": 32.0, \"max\": 44.0, \"std\": 4.35}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 11, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 32\n - Water reclamation index: 44\n - Packaging efficiency rating: 39\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.2, \"count\": 5, \"min\": 32.0, \"max\": 44.0, \"std\": 4.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 11, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 61, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 32\n - Water reclamation index: 44\n - Packaging efficiency rating: 39\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.2, \"count\": 5, \"min\": 32.0, \"max\": 44.0, \"std\": 4.35}\n\nTool: check_external_reference\nOutput: {\"request_id\": 61, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 11, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 32\n - Water reclamation index: 44\n - Packaging efficiency rating: 39\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.2, \"count\": 5, \"min\": 32.0, \"max\": 44.0, \"std\": 4.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 11, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 61, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 32\n - Water reclamation index: 44\n - Packaging efficiency rating: 39\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.2, \"count\": 5, \"min\": 32.0, \"max\": 44.0, \"std\": 4.35}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 61, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 37, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: 39\n - Water usage optimization index: 39\n - Material waste reduction rating: 38\n - Carbon footprint benchmark score: 50\n - Sustainability compliance rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 36.0, \"max\": 50.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 12, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: 39\n - Water usage optimization index: 39\n - Material waste reduction rating: 38\n - Carbon footprint benchmark score: 50\n - Sustainability compliance rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 36.0, \"max\": 50.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 12, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 62, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: 39\n - Water usage optimization index: 39\n - Material waste reduction rating: 38\n - Carbon footprint benchmark score: 50\n - Sustainability compliance rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 36.0, \"max\": 50.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"request_id\": 62, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 12, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: 39\n - Water usage optimization index: 39\n - Material waste reduction rating: 38\n - Carbon footprint benchmark score: 50\n - Sustainability compliance rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 36.0, \"max\": 50.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 12, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 62, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: 39\n - Water usage optimization index: 39\n - Material waste reduction rating: 38\n - Carbon footprint benchmark score: 50\n - Sustainability compliance rating: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 36.0, \"max\": 50.0, \"std\": 4.92}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 62, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 42\n - Emissions intensity rating: 51\n - Resource recovery score: 35\n - Environmental management system rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 5, \"min\": 35.0, \"max\": 51.0, \"std\": 5.33}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 42\n - Emissions intensity rating: 51\n - Resource recovery score: 35\n - Environmental management system rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 5, \"min\": 35.0, \"max\": 51.0, \"std\": 5.33}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 42\n - Emissions intensity rating: 51\n - Resource recovery score: 35\n - Environmental management system rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 5, \"min\": 35.0, \"max\": 51.0, \"std\": 5.33}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 42\n - Emissions intensity rating: 51\n - Resource recovery score: 35\n - Environmental management system rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 5, \"min\": 35.0, \"max\": 51.0, \"std\": 5.33}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 42\n - Emissions intensity rating: 51\n - Resource recovery score: 35\n - Environmental management system rating: 46\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 5, \"min\": 35.0, \"max\": 51.0, \"std\": 5.33}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: 74\n - Water reclamation index: 60\n - Packaging efficiency rating: 57\n - Supply chain carbon score: 58\n - Biodiversity impact rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.2, \"count\": 5, \"min\": 57.0, \"max\": 74.0, \"std\": 6.49}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 39, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: 74\n - Water reclamation index: 60\n - Packaging efficiency rating: 57\n - Supply chain carbon score: 58\n - Biodiversity impact rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.2, \"count\": 5, \"min\": 57.0, \"max\": 74.0, \"std\": 6.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 39, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 89, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: 74\n - Water reclamation index: 60\n - Packaging efficiency rating: 57\n - Supply chain carbon score: 58\n - Biodiversity impact rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.2, \"count\": 5, \"min\": 57.0, \"max\": 74.0, \"std\": 6.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 89, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 39, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: 74\n - Water reclamation index: 60\n - Packaging efficiency rating: 57\n - Supply chain carbon score: 58\n - Biodiversity impact rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.2, \"count\": 5, \"min\": 57.0, \"max\": 74.0, \"std\": 6.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 39, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 89, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: 74\n - Water reclamation index: 60\n - Packaging efficiency rating: 57\n - Supply chain carbon score: 58\n - Biodiversity impact rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.2, \"count\": 5, \"min\": 57.0, \"max\": 74.0, \"std\": 6.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 89, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 51\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 57\n - Sustainability compliance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.2, \"count\": 5, \"min\": 46.0, \"max\": 64.0, \"std\": 6.18}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 51\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 57\n - Sustainability compliance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.2, \"count\": 5, \"min\": 46.0, \"max\": 64.0, \"std\": 6.18}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 51\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 57\n - Sustainability compliance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.2, \"count\": 5, \"min\": 46.0, \"max\": 64.0, \"std\": 6.18}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 51\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 57\n - Sustainability compliance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.2, \"count\": 5, \"min\": 46.0, \"max\": 64.0, \"std\": 6.18}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 46\n - Water usage optimization index: 51\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 57\n - Sustainability compliance rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.2, \"count\": 5, \"min\": 46.0, \"max\": 64.0, \"std\": 6.18}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 59\n - Recycling rate index: 40\n - Emissions intensity rating: 47\n - Resource recovery score: 49\n - Environmental management system rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 40.0, \"max\": 59.0, \"std\": 6.82}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 59\n - Recycling rate index: 40\n - Emissions intensity rating: 47\n - Resource recovery score: 49\n - Environmental management system rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 40.0, \"max\": 59.0, \"std\": 6.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 59\n - Recycling rate index: 40\n - Emissions intensity rating: 47\n - Resource recovery score: 49\n - Environmental management system rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 40.0, \"max\": 59.0, \"std\": 6.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 59\n - Recycling rate index: 40\n - Emissions intensity rating: 47\n - Resource recovery score: 49\n - Environmental management system rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 40.0, \"max\": 59.0, \"std\": 6.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 59\n - Recycling rate index: 40\n - Emissions intensity rating: 47\n - Resource recovery score: 49\n - Environmental management system rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 40.0, \"max\": 59.0, \"std\": 6.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: 66\n - Water reclamation index: 67\n - Packaging efficiency rating: 77\n - Supply chain carbon score: 75\n - Biodiversity impact rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.4, \"count\": 5, \"min\": 52.0, \"max\": 77.0, \"std\": 8.82}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: 66\n - Water reclamation index: 67\n - Packaging efficiency rating: 77\n - Supply chain carbon score: 75\n - Biodiversity impact rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.4, \"count\": 5, \"min\": 52.0, \"max\": 77.0, \"std\": 8.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: 66\n - Water reclamation index: 67\n - Packaging efficiency rating: 77\n - Supply chain carbon score: 75\n - Biodiversity impact rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.4, \"count\": 5, \"min\": 52.0, \"max\": 77.0, \"std\": 8.82}\n\nTool: check_external_reference\nOutput: {\"request_id\": 94, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: 66\n - Water reclamation index: 67\n - Packaging efficiency rating: 77\n - Supply chain carbon score: 75\n - Biodiversity impact rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.4, \"count\": 5, \"min\": 52.0, \"max\": 77.0, \"std\": 8.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: 66\n - Water reclamation index: 67\n - Packaging efficiency rating: 77\n - Supply chain carbon score: 75\n - Biodiversity impact rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 67.4, \"count\": 5, \"min\": 52.0, \"max\": 77.0, \"std\": 8.82}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 94, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 67, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 54\n - Water usage optimization index: 82\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 55\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 54.0, \"max\": 82.0, \"std\": 10.3}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 54\n - Water usage optimization index: 82\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 55\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 54.0, \"max\": 82.0, \"std\": 10.3}\n\nTool: check_external_reference\nOutput: {\"request_id\": 34, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 54\n - Water usage optimization index: 82\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 55\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 54.0, \"max\": 82.0, \"std\": 10.3}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 54\n - Water usage optimization index: 82\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 55\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 54.0, \"max\": 82.0, \"std\": 10.3}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 34, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 54\n - Water usage optimization index: 82\n - Material waste reduction rating: 58\n - Carbon footprint benchmark score: 55\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 5, \"min\": 54.0, \"max\": 82.0, \"std\": 10.3}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 60\n - Recycling rate index: 69\n - Emissions intensity rating: 56\n - Resource recovery score: 63\n - Environmental management system rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 56.0, \"max\": 69.0, \"std\": 4.87}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 40, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 60\n - Recycling rate index: 69\n - Emissions intensity rating: 56\n - Resource recovery score: 63\n - Environmental management system rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 56.0, \"max\": 69.0, \"std\": 4.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 40, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 90, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 60\n - Recycling rate index: 69\n - Emissions intensity rating: 56\n - Resource recovery score: 63\n - Environmental management system rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 56.0, \"max\": 69.0, \"std\": 4.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 90, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 40, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 60\n - Recycling rate index: 69\n - Emissions intensity rating: 56\n - Resource recovery score: 63\n - Environmental management system rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 56.0, \"max\": 69.0, \"std\": 4.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 40, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 90, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 60\n - Recycling rate index: 69\n - Emissions intensity rating: 56\n - Resource recovery score: 63\n - Environmental management system rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 56.0, \"max\": 69.0, \"std\": 4.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 90, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 32\n - Packaging efficiency rating: 40\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.4, \"count\": 5, \"min\": 32.0, \"max\": 51.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 32\n - Packaging efficiency rating: 40\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.4, \"count\": 5, \"min\": 32.0, \"max\": 51.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 32\n - Packaging efficiency rating: 40\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.4, \"count\": 5, \"min\": 32.0, \"max\": 51.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 32\n - Packaging efficiency rating: 40\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.4, \"count\": 5, \"min\": 32.0, \"max\": 51.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 25, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 46\n - Water reclamation index: 32\n - Packaging efficiency rating: 40\n - Supply chain carbon score: 38\n - Biodiversity impact rating: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.4, \"count\": 5, \"min\": 32.0, \"max\": 51.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 62\n - Water usage optimization index: 71\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 69\n - Sustainability compliance rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 71.0, \"std\": 5.06}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 62\n - Water usage optimization index: 71\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 69\n - Sustainability compliance rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 71.0, \"std\": 5.06}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 62\n - Water usage optimization index: 71\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 69\n - Sustainability compliance rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 71.0, \"std\": 5.06}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 62\n - Water usage optimization index: 71\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 69\n - Sustainability compliance rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 71.0, \"std\": 5.06}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 62\n - Water usage optimization index: 71\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 69\n - Sustainability compliance rating: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 71.0, \"std\": 5.06}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 25\n - Recycling rate index: 34\n - Emissions intensity rating: 14\n - Resource recovery score: 32\n - Environmental management system rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 14.0, \"max\": 38.0, \"std\": 8.43}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 25\n - Recycling rate index: 34\n - Emissions intensity rating: 14\n - Resource recovery score: 32\n - Environmental management system rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 14.0, \"max\": 38.0, \"std\": 8.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 71, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 25\n - Recycling rate index: 34\n - Emissions intensity rating: 14\n - Resource recovery score: 32\n - Environmental management system rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 14.0, \"max\": 38.0, \"std\": 8.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 71, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 25\n - Recycling rate index: 34\n - Emissions intensity rating: 14\n - Resource recovery score: 32\n - Environmental management system rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 14.0, \"max\": 38.0, \"std\": 8.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 71, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 25\n - Recycling rate index: 34\n - Emissions intensity rating: 14\n - Resource recovery score: 32\n - Environmental management system rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 28.6, \"count\": 5, \"min\": 14.0, \"max\": 38.0, \"std\": 8.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 71, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 57\n - Water reclamation index: 74\n - Packaging efficiency rating: 74\n - Supply chain carbon score: 76\n - Biodiversity impact rating: 80\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 72.2, \"count\": 5, \"min\": 57.0, \"max\": 80.0, \"std\": 7.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 57\n - Water reclamation index: 74\n - Packaging efficiency rating: 74\n - Supply chain carbon score: 76\n - Biodiversity impact rating: 80\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 72.2, \"count\": 5, \"min\": 57.0, \"max\": 80.0, \"std\": 7.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 57\n - Water reclamation index: 74\n - Packaging efficiency rating: 74\n - Supply chain carbon score: 76\n - Biodiversity impact rating: 80\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 72.2, \"count\": 5, \"min\": 57.0, \"max\": 80.0, \"std\": 7.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 57\n - Water reclamation index: 74\n - Packaging efficiency rating: 74\n - Supply chain carbon score: 76\n - Biodiversity impact rating: 80\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 72.2, \"count\": 5, \"min\": 57.0, \"max\": 80.0, \"std\": 7.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 57\n - Water reclamation index: 74\n - Packaging efficiency rating: 74\n - Supply chain carbon score: 76\n - Biodiversity impact rating: 80\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 72.2, \"count\": 5, \"min\": 57.0, \"max\": 80.0, \"std\": 7.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: 26\n - Water usage optimization index: 46\n - Material waste reduction rating: 41\n - Carbon footprint benchmark score: 45\n - Sustainability compliance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 26.0, \"max\": 46.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: 26\n - Water usage optimization index: 46\n - Material waste reduction rating: 41\n - Carbon footprint benchmark score: 45\n - Sustainability compliance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 26.0, \"max\": 46.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: 26\n - Water usage optimization index: 46\n - Material waste reduction rating: 41\n - Carbon footprint benchmark score: 45\n - Sustainability compliance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 26.0, \"max\": 46.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: 26\n - Water usage optimization index: 46\n - Material waste reduction rating: 41\n - Carbon footprint benchmark score: 45\n - Sustainability compliance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 26.0, \"max\": 46.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: 26\n - Water usage optimization index: 46\n - Material waste reduction rating: 41\n - Carbon footprint benchmark score: 45\n - Sustainability compliance rating: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 26.0, \"max\": 46.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 57\n - Recycling rate index: 65\n - Emissions intensity rating: 69\n - Resource recovery score: 73\n - Environmental management system rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 57.0, \"max\": 73.0, \"std\": 5.43}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 57\n - Recycling rate index: 65\n - Emissions intensity rating: 69\n - Resource recovery score: 73\n - Environmental management system rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 57.0, \"max\": 73.0, \"std\": 5.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 57\n - Recycling rate index: 65\n - Emissions intensity rating: 69\n - Resource recovery score: 73\n - Environmental management system rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 57.0, \"max\": 73.0, \"std\": 5.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 57\n - Recycling rate index: 65\n - Emissions intensity rating: 69\n - Resource recovery score: 73\n - Environmental management system rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 57.0, \"max\": 73.0, \"std\": 5.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: 57\n - Recycling rate index: 65\n - Emissions intensity rating: 69\n - Resource recovery score: 73\n - Environmental management system rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 57.0, \"max\": 73.0, \"std\": 5.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 86\n - Water reclamation index: 63\n - Packaging efficiency rating: 56\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 78\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 70.2, \"count\": 5, \"min\": 56.0, \"max\": 86.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 86\n - Water reclamation index: 63\n - Packaging efficiency rating: 56\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 78\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 70.2, \"count\": 5, \"min\": 56.0, \"max\": 86.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 86\n - Water reclamation index: 63\n - Packaging efficiency rating: 56\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 78\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 70.2, \"count\": 5, \"min\": 56.0, \"max\": 86.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 86\n - Water reclamation index: 63\n - Packaging efficiency rating: 56\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 78\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 70.2, \"count\": 5, \"min\": 56.0, \"max\": 86.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: 86\n - Water reclamation index: 63\n - Packaging efficiency rating: 56\n - Supply chain carbon score: 68\n - Biodiversity impact rating: 78\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 70.2, \"count\": 5, \"min\": 56.0, \"max\": 86.0, \"std\": 10.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 70, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 53\n - Water usage optimization index: 66\n - Material waste reduction rating: 50\n - Carbon footprint benchmark score: 34\n - Sustainability compliance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 10.33}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 53\n - Water usage optimization index: 66\n - Material waste reduction rating: 50\n - Carbon footprint benchmark score: 34\n - Sustainability compliance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 10.33}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 95, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 53\n - Water usage optimization index: 66\n - Material waste reduction rating: 50\n - Carbon footprint benchmark score: 34\n - Sustainability compliance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 10.33}\n\nTool: check_external_reference\nOutput: {\"request_id\": 95, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 53\n - Water usage optimization index: 66\n - Material waste reduction rating: 50\n - Carbon footprint benchmark score: 34\n - Sustainability compliance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 10.33}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 95, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: 53\n - Water usage optimization index: 66\n - Material waste reduction rating: 50\n - Carbon footprint benchmark score: 34\n - Sustainability compliance rating: 55\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.6, \"count\": 5, \"min\": 34.0, \"max\": 66.0, \"std\": 10.33}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 95, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 61\n - Recycling rate index: 73\n - Emissions intensity rating: 49\n - Resource recovery score: 70\n - Environmental management system rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.4, \"count\": 5, \"min\": 49.0, \"max\": 73.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 61\n - Recycling rate index: 73\n - Emissions intensity rating: 49\n - Resource recovery score: 70\n - Environmental management system rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.4, \"count\": 5, \"min\": 49.0, \"max\": 73.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 61\n - Recycling rate index: 73\n - Emissions intensity rating: 49\n - Resource recovery score: 70\n - Environmental management system rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.4, \"count\": 5, \"min\": 49.0, \"max\": 73.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 61\n - Recycling rate index: 73\n - Emissions intensity rating: 49\n - Resource recovery score: 70\n - Environmental management system rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.4, \"count\": 5, \"min\": 49.0, \"max\": 73.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: 61\n - Recycling rate index: 73\n - Emissions intensity rating: 49\n - Resource recovery score: 70\n - Environmental management system rating: 69\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.4, \"count\": 5, \"min\": 49.0, \"max\": 73.0, \"std\": 8.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 37\n - Water reclamation index: 47\n - Packaging efficiency rating: 30\n - Supply chain carbon score: 44\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 5, \"min\": 30.0, \"max\": 47.0, \"std\": 5.97}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 3, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 37\n - Water reclamation index: 47\n - Packaging efficiency rating: 30\n - Supply chain carbon score: 44\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 5, \"min\": 30.0, \"max\": 47.0, \"std\": 5.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 3, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 83, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 37\n - Water reclamation index: 47\n - Packaging efficiency rating: 30\n - Supply chain carbon score: 44\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 5, \"min\": 30.0, \"max\": 47.0, \"std\": 5.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 83, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 3, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 37\n - Water reclamation index: 47\n - Packaging efficiency rating: 30\n - Supply chain carbon score: 44\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 5, \"min\": 30.0, \"max\": 47.0, \"std\": 5.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 3, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 83, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: 37\n - Water reclamation index: 47\n - Packaging efficiency rating: 30\n - Supply chain carbon score: 44\n - Biodiversity impact rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 5, \"min\": 30.0, \"max\": 47.0, \"std\": 5.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 83, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 63\n - Water usage optimization index: 60\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 77\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 63\n - Water usage optimization index: 60\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 77\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 63\n - Water usage optimization index: 60\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 77\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 63\n - Water usage optimization index: 60\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 77\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-e-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: 63\n - Water usage optimization index: 60\n - Material waste reduction rating: 59\n - Carbon footprint benchmark score: 77\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 28\n - Emissions intensity rating: 48\n - Resource recovery score: [data not available]\n - Environmental management system rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 3, \"min\": 28.0, \"max\": 48.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 28\n - Emissions intensity rating: 48\n - Resource recovery score: [data not available]\n - Environmental management system rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 3, \"min\": 28.0, \"max\": 48.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 28\n - Emissions intensity rating: 48\n - Resource recovery score: [data not available]\n - Environmental management system rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 3, \"min\": 28.0, \"max\": 48.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"request_id\": 62, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 28\n - Emissions intensity rating: 48\n - Resource recovery score: [data not available]\n - Environmental management system rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 3, \"min\": 28.0, \"max\": 48.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 28\n - Emissions intensity rating: 48\n - Resource recovery score: [data not available]\n - Environmental management system rating: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.0, \"count\": 3, \"min\": 28.0, \"max\": 48.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 62, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 35, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 34\n - Packaging efficiency rating: 76\n - Supply chain carbon score: 40\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 34.0, \"max\": 76.0, \"std\": 18.55}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 34\n - Packaging efficiency rating: 76\n - Supply chain carbon score: 40\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 34.0, \"max\": 76.0, \"std\": 18.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 34\n - Packaging efficiency rating: 76\n - Supply chain carbon score: 40\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 34.0, \"max\": 76.0, \"std\": 18.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 34\n - Packaging efficiency rating: 76\n - Supply chain carbon score: 40\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 34.0, \"max\": 76.0, \"std\": 18.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 34\n - Packaging efficiency rating: 76\n - Supply chain carbon score: 40\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 34.0, \"max\": 76.0, \"std\": 18.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 20\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 20.0, \"max\": 72.0, \"std\": 22.38}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 20\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 20.0, \"max\": 72.0, \"std\": 22.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 49, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 20\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 20.0, \"max\": 72.0, \"std\": 22.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 49, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 20\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 20.0, \"max\": 72.0, \"std\": 22.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 49, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 20\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 20.0, \"max\": 72.0, \"std\": 22.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 49, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 89\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 20\n - Environmental management system rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 20.0, \"max\": 89.0, \"std\": 28.69}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 89\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 20\n - Environmental management system rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 20.0, \"max\": 89.0, \"std\": 28.69}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 89\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 20\n - Environmental management system rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 20.0, \"max\": 89.0, \"std\": 28.69}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 89\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 20\n - Environmental management system rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 20.0, \"max\": 89.0, \"std\": 28.69}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 89\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 20\n - Environmental management system rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 20.0, \"max\": 89.0, \"std\": 28.69}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 35\n - Water reclamation index: 79\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 7\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 7.0, \"max\": 79.0, \"std\": 29.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 35\n - Water reclamation index: 79\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 7\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 7.0, \"max\": 79.0, \"std\": 29.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 35\n - Water reclamation index: 79\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 7\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 7.0, \"max\": 79.0, \"std\": 29.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 35\n - Water reclamation index: 79\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 7\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 7.0, \"max\": 79.0, \"std\": 29.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 35\n - Water reclamation index: 79\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 7\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 7.0, \"max\": 79.0, \"std\": 29.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 43\n - Material waste reduction rating: 57\n - Carbon footprint benchmark score: 56\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 43, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 43\n - Material waste reduction rating: 57\n - Carbon footprint benchmark score: 56\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 43, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 73, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 43\n - Material waste reduction rating: 57\n - Carbon footprint benchmark score: 56\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"request_id\": 73, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 43, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 43\n - Material waste reduction rating: 57\n - Carbon footprint benchmark score: 56\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 43, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 73, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 43\n - Material waste reduction rating: 57\n - Carbon footprint benchmark score: 56\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 43.0, \"max\": 57.0, \"std\": 6.38}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 73, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 86\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 63\n - Resource recovery score: 23\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 23.0, \"max\": 86.0, \"std\": 26.03}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 86\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 63\n - Resource recovery score: 23\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 23.0, \"max\": 86.0, \"std\": 26.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 86\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 63\n - Resource recovery score: 23\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 23.0, \"max\": 86.0, \"std\": 26.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 86\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 63\n - Resource recovery score: 23\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 23.0, \"max\": 86.0, \"std\": 26.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Renewable energy adoption score: 86\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 63\n - Resource recovery score: 23\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 23.0, \"max\": 86.0, \"std\": 26.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 54\n - Supply chain carbon score: 57\n - Biodiversity impact rating: 12\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 12.0, \"max\": 57.0, \"std\": 20.54}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 54\n - Supply chain carbon score: 57\n - Biodiversity impact rating: 12\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 12.0, \"max\": 57.0, \"std\": 20.54}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 54\n - Supply chain carbon score: 57\n - Biodiversity impact rating: 12\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 12.0, \"max\": 57.0, \"std\": 20.54}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 54\n - Supply chain carbon score: 57\n - Biodiversity impact rating: 12\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 12.0, \"max\": 57.0, \"std\": 20.54}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 54\n - Supply chain carbon score: 57\n - Biodiversity impact rating: 12\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 12.0, \"max\": 57.0, \"std\": 20.54}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 32\n - Water usage optimization index: 73\n - Material waste reduction rating: 47\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 32.0, \"max\": 73.0, \"std\": 16.94}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 48, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 32\n - Water usage optimization index: 73\n - Material waste reduction rating: 47\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 32.0, \"max\": 73.0, \"std\": 16.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 48, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 32\n - Water usage optimization index: 73\n - Material waste reduction rating: 47\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 32.0, \"max\": 73.0, \"std\": 16.94}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 48, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 32\n - Water usage optimization index: 73\n - Material waste reduction rating: 47\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 32.0, \"max\": 73.0, \"std\": 16.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 48, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 32\n - Water usage optimization index: 73\n - Material waste reduction rating: 47\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 32.0, \"max\": 73.0, \"std\": 16.94}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 40\n - Emissions intensity rating: 86\n - Resource recovery score: [data not available]\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 86.0, \"std\": 21.45}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 40\n - Emissions intensity rating: 86\n - Resource recovery score: [data not available]\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 86.0, \"std\": 21.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 56, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 40\n - Emissions intensity rating: 86\n - Resource recovery score: [data not available]\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 86.0, \"std\": 21.45}\n\nTool: check_external_reference\nOutput: {\"request_id\": 56, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 40\n - Emissions intensity rating: 86\n - Resource recovery score: [data not available]\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 86.0, \"std\": 21.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 56, "offset": 15, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Renewable energy adoption score: 41\n - Recycling rate index: 40\n - Emissions intensity rating: 86\n - Resource recovery score: [data not available]\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 40.0, \"max\": 86.0, \"std\": 21.45}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 56, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 44\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 54\n - Biodiversity impact rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 44.0, \"max\": 56.0, \"std\": 5.25}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 44\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 54\n - Biodiversity impact rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 44.0, \"max\": 56.0, \"std\": 5.25}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 44\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 54\n - Biodiversity impact rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 44.0, \"max\": 56.0, \"std\": 5.25}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 44\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 54\n - Biodiversity impact rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 44.0, \"max\": 56.0, \"std\": 5.25}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 44\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 54\n - Biodiversity impact rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.33, \"count\": 3, \"min\": 44.0, \"max\": 56.0, \"std\": 5.25}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 44\n - Water usage optimization index: 37\n - Material waste reduction rating: 18\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 44.0, \"std\": 10.98}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 44\n - Water usage optimization index: 37\n - Material waste reduction rating: 18\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 44.0, \"std\": 10.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 44\n - Water usage optimization index: 37\n - Material waste reduction rating: 18\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 44.0, \"std\": 10.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 44\n - Water usage optimization index: 37\n - Material waste reduction rating: 18\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 44.0, \"std\": 10.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Energy efficiency audit score: 44\n - Water usage optimization index: 37\n - Material waste reduction rating: 18\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 44.0, \"std\": 10.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 56\n - Recycling rate index: 48\n - Emissions intensity rating: [data not available]\n - Resource recovery score: [data not available]\n - Environmental management system rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.0, \"count\": 3, \"min\": 37.0, \"max\": 56.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 56\n - Recycling rate index: 48\n - Emissions intensity rating: [data not available]\n - Resource recovery score: [data not available]\n - Environmental management system rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.0, \"count\": 3, \"min\": 37.0, \"max\": 56.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 34, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 56\n - Recycling rate index: 48\n - Emissions intensity rating: [data not available]\n - Resource recovery score: [data not available]\n - Environmental management system rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.0, \"count\": 3, \"min\": 37.0, \"max\": 56.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 34, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 56\n - Recycling rate index: 48\n - Emissions intensity rating: [data not available]\n - Resource recovery score: [data not available]\n - Environmental management system rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.0, \"count\": 3, \"min\": 37.0, \"max\": 56.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 34, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Renewable energy adoption score: 56\n - Recycling rate index: 48\n - Emissions intensity rating: [data not available]\n - Resource recovery score: [data not available]\n - Environmental management system rating: 37\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.0, \"count\": 3, \"min\": 37.0, \"max\": 56.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 42\n - Water reclamation index: 80\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 38\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 38.0, \"max\": 80.0, \"std\": 18.93}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 42\n - Water reclamation index: 80\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 38\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 38.0, \"max\": 80.0, \"std\": 18.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 42\n - Water reclamation index: 80\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 38\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 38.0, \"max\": 80.0, \"std\": 18.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 42\n - Water reclamation index: 80\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 38\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 38.0, \"max\": 80.0, \"std\": 18.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Power consumption per unit score: 42\n - Water reclamation index: 80\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 38\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 38.0, \"max\": 80.0, \"std\": 18.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 50\n - Material waste reduction rating: 30\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 30.0, \"max\": 72.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 50\n - Material waste reduction rating: 30\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 30.0, \"max\": 72.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 50\n - Material waste reduction rating: 30\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 30.0, \"max\": 72.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 94, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 50\n - Material waste reduction rating: 30\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 30.0, \"max\": 72.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Energy efficiency audit score: 72\n - Water usage optimization index: 50\n - Material waste reduction rating: 30\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 30.0, \"max\": 72.0, \"std\": 17.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 94, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 68\n - Recycling rate index: 44\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 31\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 31.0, \"max\": 68.0, \"std\": 15.33}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 38, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 68\n - Recycling rate index: 44\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 31\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 31.0, \"max\": 68.0, \"std\": 15.33}\n\nTool: check_external_reference\nOutput: {\"request_id\": 38, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 88, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 68\n - Recycling rate index: 44\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 31\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 31.0, \"max\": 68.0, \"std\": 15.33}\n\nTool: check_external_reference\nOutput: {\"request_id\": 88, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 38, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 68\n - Recycling rate index: 44\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 31\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 31.0, \"max\": 68.0, \"std\": 15.33}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 38, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 88, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Renewable energy adoption score: 68\n - Recycling rate index: 44\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 31\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 31.0, \"max\": 68.0, \"std\": 15.33}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 88, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 58\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 45\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 45.0, \"max\": 58.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 58\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 45\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 45.0, \"max\": 58.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 58\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 45\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 45.0, \"max\": 58.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 58\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 45\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 45.0, \"max\": 58.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Power consumption per unit score: 58\n - Water reclamation index: [data not available]\n - Packaging efficiency rating: 45\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.0, \"count\": 3, \"min\": 45.0, \"max\": 58.0, \"std\": 5.72}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: [data not available]\n - Material waste reduction rating: 62\n - Carbon footprint benchmark score: 41\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 41.0, \"max\": 62.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 43, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: [data not available]\n - Material waste reduction rating: 62\n - Carbon footprint benchmark score: 41\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 41.0, \"max\": 62.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 43, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 93, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: [data not available]\n - Material waste reduction rating: 62\n - Carbon footprint benchmark score: 41\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 41.0, \"max\": 62.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 93, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 43, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: [data not available]\n - Material waste reduction rating: 62\n - Carbon footprint benchmark score: 41\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 41.0, \"max\": 62.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 43, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 93, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: [data not available]\n - Material waste reduction rating: 62\n - Carbon footprint benchmark score: 41\n - Sustainability compliance rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.67, \"count\": 3, \"min\": 41.0, \"max\": 62.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 93, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 6\n - Emissions intensity rating: 73\n - Resource recovery score: 10\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 6.0, \"max\": 73.0, \"std\": 30.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 6\n - Emissions intensity rating: 73\n - Resource recovery score: 10\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 6.0, \"max\": 73.0, \"std\": 30.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 58, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 6\n - Emissions intensity rating: 73\n - Resource recovery score: 10\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 6.0, \"max\": 73.0, \"std\": 30.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 58, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 6\n - Emissions intensity rating: 73\n - Resource recovery score: 10\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 6.0, \"max\": 73.0, \"std\": 30.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 58, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 6\n - Emissions intensity rating: 73\n - Resource recovery score: 10\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 6.0, \"max\": 73.0, \"std\": 30.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 58, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 58\n - Packaging efficiency rating: 31\n - Supply chain carbon score: 72\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 31.0, \"max\": 72.0, \"std\": 17.02}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 58\n - Packaging efficiency rating: 31\n - Supply chain carbon score: 72\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 31.0, \"max\": 72.0, \"std\": 17.02}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 58\n - Packaging efficiency rating: 31\n - Supply chain carbon score: 72\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 31.0, \"max\": 72.0, \"std\": 17.02}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 58\n - Packaging efficiency rating: 31\n - Supply chain carbon score: 72\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 31.0, \"max\": 72.0, \"std\": 17.02}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 58\n - Packaging efficiency rating: 31\n - Supply chain carbon score: 72\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 31.0, \"max\": 72.0, \"std\": 17.02}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 52\n - Carbon footprint benchmark score: 64\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 36.0, \"max\": 64.0, \"std\": 11.47}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 52\n - Carbon footprint benchmark score: 64\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 36.0, \"max\": 64.0, \"std\": 11.47}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 52\n - Carbon footprint benchmark score: 64\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 36.0, \"max\": 64.0, \"std\": 11.47}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 52\n - Carbon footprint benchmark score: 64\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 36.0, \"max\": 64.0, \"std\": 11.47}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 52\n - Carbon footprint benchmark score: 64\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 36.0, \"max\": 64.0, \"std\": 11.47}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 74\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 72\n - Environmental management system rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.67, \"count\": 3, \"min\": 42.0, \"max\": 74.0, \"std\": 14.64}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 74\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 72\n - Environmental management system rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.67, \"count\": 3, \"min\": 42.0, \"max\": 74.0, \"std\": 14.64}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 74\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 72\n - Environmental management system rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.67, \"count\": 3, \"min\": 42.0, \"max\": 74.0, \"std\": 14.64}\n\nTool: check_external_reference\nOutput: {\"request_id\": 75, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 74\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 72\n - Environmental management system rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.67, \"count\": 3, \"min\": 42.0, \"max\": 74.0, \"std\": 14.64}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 75, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Renewable energy adoption score: [data not available]\n - Recycling rate index: 74\n - Emissions intensity rating: [data not available]\n - Resource recovery score: 72\n - Environmental management system rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.67, \"count\": 3, \"min\": 42.0, \"max\": 74.0, \"std\": 14.64}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 75, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 45\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 18\n - Biodiversity impact rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 18.0, \"max\": 58.0, \"std\": 16.66}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 45\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 18\n - Biodiversity impact rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 18.0, \"max\": 58.0, \"std\": 16.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 45\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 18\n - Biodiversity impact rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 18.0, \"max\": 58.0, \"std\": 16.66}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 45\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 18\n - Biodiversity impact rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 18.0, \"max\": 58.0, \"std\": 16.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA data center is assessing its energy and resource consumption efficiency. Audit scores (0–100) are as follows.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 45\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 18\n - Biodiversity impact rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.33, \"count\": 3, \"min\": 18.0, \"max\": 58.0, \"std\": 16.66}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 45\n - Carbon footprint benchmark score: 18\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 45.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 45\n - Carbon footprint benchmark score: 18\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 45.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 96, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 45\n - Carbon footprint benchmark score: 18\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 45.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 96, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 45\n - Carbon footprint benchmark score: 18\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 45.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 96, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA university campus is reviewing resource consumption patterns. Five independent assessments yielded the following scores.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 36\n - Material waste reduction rating: 45\n - Carbon footprint benchmark score: 18\n - Sustainability compliance rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.0, \"count\": 3, \"min\": 18.0, \"max\": 45.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 96, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 33, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 90\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 58\n - Resource recovery score: [data not available]\n - Environmental management system rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 43.0, \"max\": 90.0, \"std\": 19.6}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 90\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 58\n - Resource recovery score: [data not available]\n - Environmental management system rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 43.0, \"max\": 90.0, \"std\": 19.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 90\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 58\n - Resource recovery score: [data not available]\n - Environmental management system rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 43.0, \"max\": 90.0, \"std\": 19.6}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 90\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 58\n - Resource recovery score: [data not available]\n - Environmental management system rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 43.0, \"max\": 90.0, \"std\": 19.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA municipal utility is evaluating resource management efficiency. Ratings from five evaluation frameworks are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 90\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 58\n - Resource recovery score: [data not available]\n - Environmental management system rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 43.0, \"max\": 90.0, \"std\": 19.6}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 29\n - Packaging efficiency rating: 53\n - Supply chain carbon score: [data not available]\n - Biodiversity impact rating: 7\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 7.0, \"max\": 53.0, \"std\": 18.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 29\n - Packaging efficiency rating: 53\n - Supply chain carbon score: [data not available]\n - Biodiversity impact rating: 7\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 7.0, \"max\": 53.0, \"std\": 18.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 73, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 29\n - Packaging efficiency rating: 53\n - Supply chain carbon score: [data not available]\n - Biodiversity impact rating: 7\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 7.0, \"max\": 53.0, \"std\": 18.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 73, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 29\n - Packaging efficiency rating: 53\n - Supply chain carbon score: [data not available]\n - Biodiversity impact rating: 7\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 7.0, \"max\": 53.0, \"std\": 18.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 73, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA commercial real estate portfolio is assessing building resource efficiency. Five audit results are available.\n\nEvidence:\n - Power consumption per unit score: [data not available]\n - Water reclamation index: 29\n - Packaging efficiency rating: 53\n - Supply chain carbon score: [data not available]\n - Biodiversity impact rating: 7\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.67, \"count\": 3, \"min\": 7.0, \"max\": 53.0, \"std\": 18.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 73, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 30, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 32\n - Material waste reduction rating: 44\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 32.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 32\n - Material waste reduction rating: 44\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 32.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"request_id\": 7, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 32\n - Material waste reduction rating: 44\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 32.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"request_id\": 87, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 32\n - Material waste reduction rating: 44\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 32.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 7, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA chemical processing facility is reviewing its environmental efficiency performance. The following ratings were collected.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 32\n - Material waste reduction rating: 44\n - Carbon footprint benchmark score: [data not available]\n - Sustainability compliance rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 32.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 87, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven these assessment scores, what is your best estimate for the resource efficiency rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 92\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 60\n - Resource recovery score: 40\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 3, \"min\": 40.0, \"max\": 92.0, \"std\": 21.42}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 92\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 60\n - Resource recovery score: 40\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 3, \"min\": 40.0, \"max\": 92.0, \"std\": 21.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 92\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 60\n - Resource recovery score: 40\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 3, \"min\": 40.0, \"max\": 92.0, \"std\": 21.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 92\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 60\n - Resource recovery score: 40\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 3, \"min\": 40.0, \"max\": 92.0, \"std\": 21.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA hospital network is evaluating resource consumption across its facilities. Five sustainability scores are summarized below.\n\nEvidence:\n - Renewable energy adoption score: 92\n - Recycling rate index: [data not available]\n - Emissions intensity rating: 60\n - Resource recovery score: 40\n - Environmental management system rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.0, \"count\": 3, \"min\": 40.0, \"max\": 92.0, \"std\": 21.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite resource efficiency index (0–100) from the data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 56\n - Water reclamation index: 40\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 40.0, \"max\": 56.0, \"std\": 6.55}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 56\n - Water reclamation index: 40\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 40.0, \"max\": 56.0, \"std\": 6.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 56\n - Water reclamation index: 40\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 40.0, \"max\": 56.0, \"std\": 6.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 88, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 56\n - Water reclamation index: 40\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 40.0, \"max\": 56.0, \"std\": 6.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn agricultural cooperative is assessing resource utilization efficiency. Independent audit scores are as follows.\n\nEvidence:\n - Power consumption per unit score: 56\n - Water reclamation index: 40\n - Packaging efficiency rating: [data not available]\n - Supply chain carbon score: 47\n - Biodiversity impact rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.67, \"count\": 3, \"min\": 40.0, \"max\": 56.0, \"std\": 6.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 88, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall resource efficiency score (0–100) do you estimate based on these audits?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 25\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: 91\n - Sustainability compliance rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 25.0, \"max\": 91.0, \"std\": 27.74}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 25\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: 91\n - Sustainability compliance rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 25.0, \"max\": 91.0, \"std\": 27.74}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 25\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: 91\n - Sustainability compliance rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 25.0, \"max\": 91.0, \"std\": 27.74}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 25\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: 91\n - Sustainability compliance rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 25.0, \"max\": 91.0, \"std\": 27.74}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-resource_consumption-h-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "resource_consumption", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA manufacturing plant is evaluating its resource efficiency. Five sustainability audits have been completed.\n\nEvidence:\n - Energy efficiency audit score: [data not available]\n - Water usage optimization index: 25\n - Material waste reduction rating: [data not available]\n - Carbon footprint benchmark score: 91\n - Sustainability compliance rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 25.0, \"max\": 91.0, \"std\": 27.74}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on the audit data, estimate the overall resource efficiency index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 37\n - Channel readiness index: 55\n - Price-value perception rating: 34\n - Referral likelihood score: 42\n - Category awareness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.2, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 37\n - Channel readiness index: 55\n - Price-value perception rating: 34\n - Referral likelihood score: 42\n - Category awareness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.2, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 32, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 37\n - Channel readiness index: 55\n - Price-value perception rating: 34\n - Referral likelihood score: 42\n - Category awareness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.2, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 62, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 32, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 37\n - Channel readiness index: 55\n - Price-value perception rating: 34\n - Referral likelihood score: 42\n - Category awareness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.2, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 32, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 62, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 37\n - Channel readiness index: 55\n - Price-value perception rating: 34\n - Referral likelihood score: 42\n - Category awareness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.2, \"count\": 5, \"min\": 34.0, \"max\": 63.0, \"std\": 11.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 62, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 38\n - Retail shelf readiness rating: 36\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.0, \"count\": 5, \"min\": 36.0, \"max\": 47.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 38\n - Retail shelf readiness rating: 36\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.0, \"count\": 5, \"min\": 36.0, \"max\": 47.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 38\n - Retail shelf readiness rating: 36\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.0, \"count\": 5, \"min\": 36.0, \"max\": 47.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 38\n - Retail shelf readiness rating: 36\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.0, \"count\": 5, \"min\": 36.0, \"max\": 47.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 38\n - Retail shelf readiness rating: 36\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.0, \"count\": 5, \"min\": 36.0, \"max\": 47.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 54\n - Market penetration index: 41\n - Demographic fit rating: 45\n - Competitive landscape score: 45\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 31.0, \"max\": 54.0, \"std\": 7.44}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 54\n - Market penetration index: 41\n - Demographic fit rating: 45\n - Competitive landscape score: 45\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 31.0, \"max\": 54.0, \"std\": 7.44}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 58, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 54\n - Market penetration index: 41\n - Demographic fit rating: 45\n - Competitive landscape score: 45\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 31.0, \"max\": 54.0, \"std\": 7.44}\n\nTool: check_external_reference\nOutput: {\"request_id\": 58, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 54\n - Market penetration index: 41\n - Demographic fit rating: 45\n - Competitive landscape score: 45\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 31.0, \"max\": 54.0, \"std\": 7.44}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 58, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 54\n - Market penetration index: 41\n - Demographic fit rating: 45\n - Competitive landscape score: 45\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 31.0, \"max\": 54.0, \"std\": 7.44}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 58, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 48\n - Price-value perception rating: 50\n - Referral likelihood score: 49\n - Category awareness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.6, \"count\": 5, \"min\": 42.0, \"max\": 50.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 48\n - Price-value perception rating: 50\n - Referral likelihood score: 49\n - Category awareness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.6, \"count\": 5, \"min\": 42.0, \"max\": 50.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 36, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 48\n - Price-value perception rating: 50\n - Referral likelihood score: 49\n - Category awareness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.6, \"count\": 5, \"min\": 42.0, \"max\": 50.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 48\n - Price-value perception rating: 50\n - Referral likelihood score: 49\n - Category awareness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.6, \"count\": 5, \"min\": 42.0, \"max\": 50.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 36, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 48\n - Price-value perception rating: 50\n - Referral likelihood score: 49\n - Category awareness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.6, \"count\": 5, \"min\": 42.0, \"max\": 50.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 64\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 66\n - Geographic coverage score: 65\n - Customer lifetime value predictor: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 59.0, \"max\": 73.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 54, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 64\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 66\n - Geographic coverage score: 65\n - Customer lifetime value predictor: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 59.0, \"max\": 73.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 54, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 64\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 66\n - Geographic coverage score: 65\n - Customer lifetime value predictor: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 59.0, \"max\": 73.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 54, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 64\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 66\n - Geographic coverage score: 65\n - Customer lifetime value predictor: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 59.0, \"max\": 73.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 54, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 64\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 66\n - Geographic coverage score: 65\n - Customer lifetime value predictor: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 59.0, \"max\": 73.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 41\n - Market penetration index: 28\n - Demographic fit rating: 38\n - Competitive landscape score: 36\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.4, \"count\": 5, \"min\": 28.0, \"max\": 41.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 41\n - Market penetration index: 28\n - Demographic fit rating: 38\n - Competitive landscape score: 36\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.4, \"count\": 5, \"min\": 28.0, \"max\": 41.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 51, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 41\n - Market penetration index: 28\n - Demographic fit rating: 38\n - Competitive landscape score: 36\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.4, \"count\": 5, \"min\": 28.0, \"max\": 41.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 51, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 41\n - Market penetration index: 28\n - Demographic fit rating: 38\n - Competitive landscape score: 36\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.4, \"count\": 5, \"min\": 28.0, \"max\": 41.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 51, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 41\n - Market penetration index: 28\n - Demographic fit rating: 38\n - Competitive landscape score: 36\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.4, \"count\": 5, \"min\": 28.0, \"max\": 41.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 51, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: 72\n - Channel readiness index: 62\n - Price-value perception rating: 69\n - Referral likelihood score: 58\n - Category awareness rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 58.0, \"max\": 72.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 55, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: 72\n - Channel readiness index: 62\n - Price-value perception rating: 69\n - Referral likelihood score: 58\n - Category awareness rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 58.0, \"max\": 72.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: 72\n - Channel readiness index: 62\n - Price-value perception rating: 69\n - Referral likelihood score: 58\n - Category awareness rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 58.0, \"max\": 72.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 55, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: 72\n - Channel readiness index: 62\n - Price-value perception rating: 69\n - Referral likelihood score: 58\n - Category awareness rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 58.0, \"max\": 72.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: 72\n - Channel readiness index: 62\n - Price-value perception rating: 69\n - Referral likelihood score: 58\n - Category awareness rating: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 65.4, \"count\": 5, \"min\": 58.0, \"max\": 72.0, \"std\": 4.96}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 44\n - Social media sentiment index: 54\n - Retail shelf readiness rating: 68\n - Geographic coverage score: 69\n - Customer lifetime value predictor: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 44.0, \"max\": 69.0, \"std\": 9.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 39, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 44\n - Social media sentiment index: 54\n - Retail shelf readiness rating: 68\n - Geographic coverage score: 69\n - Customer lifetime value predictor: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 44.0, \"max\": 69.0, \"std\": 9.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 39, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 44\n - Social media sentiment index: 54\n - Retail shelf readiness rating: 68\n - Geographic coverage score: 69\n - Customer lifetime value predictor: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 44.0, \"max\": 69.0, \"std\": 9.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 39, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 44\n - Social media sentiment index: 54\n - Retail shelf readiness rating: 68\n - Geographic coverage score: 69\n - Customer lifetime value predictor: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 44.0, \"max\": 69.0, \"std\": 9.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 39, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 44\n - Social media sentiment index: 54\n - Retail shelf readiness rating: 68\n - Geographic coverage score: 69\n - Customer lifetime value predictor: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 44.0, \"max\": 69.0, \"std\": 9.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: 44\n - Demographic fit rating: 39\n - Competitive landscape score: 40\n - Brand awareness index: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 53.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: 44\n - Demographic fit rating: 39\n - Competitive landscape score: 40\n - Brand awareness index: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 53.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: 44\n - Demographic fit rating: 39\n - Competitive landscape score: 40\n - Brand awareness index: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 53.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: 44\n - Demographic fit rating: 39\n - Competitive landscape score: 40\n - Brand awareness index: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 53.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: 44\n - Demographic fit rating: 39\n - Competitive landscape score: 40\n - Brand awareness index: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.2, \"count\": 5, \"min\": 39.0, \"max\": 53.0, \"std\": 5.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 34\n - Price-value perception rating: 35\n - Referral likelihood score: 30\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 30.0, \"max\": 49.0, \"std\": 6.53}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 34\n - Price-value perception rating: 35\n - Referral likelihood score: 30\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 30.0, \"max\": 49.0, \"std\": 6.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 53, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 34\n - Price-value perception rating: 35\n - Referral likelihood score: 30\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 30.0, \"max\": 49.0, \"std\": 6.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 53, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 34\n - Price-value perception rating: 35\n - Referral likelihood score: 30\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 30.0, \"max\": 49.0, \"std\": 6.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 53, "offset": 15, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 49\n - Channel readiness index: 34\n - Price-value perception rating: 35\n - Referral likelihood score: 30\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 30.0, \"max\": 49.0, \"std\": 6.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 53, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 27\n - Retail shelf readiness rating: 48\n - Geographic coverage score: 42\n - Customer lifetime value predictor: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 27.0, \"max\": 48.0, \"std\": 9.13}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 27\n - Retail shelf readiness rating: 48\n - Geographic coverage score: 42\n - Customer lifetime value predictor: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 27.0, \"max\": 48.0, \"std\": 9.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 65, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 27\n - Retail shelf readiness rating: 48\n - Geographic coverage score: 42\n - Customer lifetime value predictor: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 27.0, \"max\": 48.0, \"std\": 9.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 65, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 27\n - Retail shelf readiness rating: 48\n - Geographic coverage score: 42\n - Customer lifetime value predictor: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 27.0, \"max\": 48.0, \"std\": 9.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 65, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: 47\n - Social media sentiment index: 27\n - Retail shelf readiness rating: 48\n - Geographic coverage score: 42\n - Customer lifetime value predictor: 28\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.4, \"count\": 5, \"min\": 27.0, \"max\": 48.0, \"std\": 9.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 65, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 44\n - Market penetration index: 58\n - Demographic fit rating: 47\n - Competitive landscape score: 56\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.8, \"count\": 5, \"min\": 39.0, \"max\": 58.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 44\n - Market penetration index: 58\n - Demographic fit rating: 47\n - Competitive landscape score: 56\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.8, \"count\": 5, \"min\": 39.0, \"max\": 58.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 44\n - Market penetration index: 58\n - Demographic fit rating: 47\n - Competitive landscape score: 56\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.8, \"count\": 5, \"min\": 39.0, \"max\": 58.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 44\n - Market penetration index: 58\n - Demographic fit rating: 47\n - Competitive landscape score: 56\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.8, \"count\": 5, \"min\": 39.0, \"max\": 58.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 44\n - Market penetration index: 58\n - Demographic fit rating: 47\n - Competitive landscape score: 56\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.8, \"count\": 5, \"min\": 39.0, \"max\": 58.0, \"std\": 7.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: 78\n - Channel readiness index: 58\n - Price-value perception rating: 52\n - Referral likelihood score: 55\n - Category awareness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.4, \"count\": 5, \"min\": 52.0, \"max\": 78.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: 78\n - Channel readiness index: 58\n - Price-value perception rating: 52\n - Referral likelihood score: 55\n - Category awareness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.4, \"count\": 5, \"min\": 52.0, \"max\": 78.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"request_id\": 35, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: 78\n - Channel readiness index: 58\n - Price-value perception rating: 52\n - Referral likelihood score: 55\n - Category awareness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.4, \"count\": 5, \"min\": 52.0, \"max\": 78.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: 78\n - Channel readiness index: 58\n - Price-value perception rating: 52\n - Referral likelihood score: 55\n - Category awareness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.4, \"count\": 5, \"min\": 52.0, \"max\": 78.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 35, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: 78\n - Channel readiness index: 58\n - Price-value perception rating: 52\n - Referral likelihood score: 55\n - Category awareness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.4, \"count\": 5, \"min\": 52.0, \"max\": 78.0, \"std\": 9.2}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 50\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 52\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.8, \"count\": 5, \"min\": 36.0, \"max\": 60.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 50\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 52\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.8, \"count\": 5, \"min\": 36.0, \"max\": 60.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 50\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 52\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.8, \"count\": 5, \"min\": 36.0, \"max\": 60.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 50\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 52\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.8, \"count\": 5, \"min\": 36.0, \"max\": 60.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 50\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 52\n - Geographic coverage score: 36\n - Customer lifetime value predictor: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.8, \"count\": 5, \"min\": 36.0, \"max\": 60.0, \"std\": 7.76}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: 40\n - Market penetration index: 51\n - Demographic fit rating: 34\n - Competitive landscape score: 40\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.8, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: 40\n - Market penetration index: 51\n - Demographic fit rating: 34\n - Competitive landscape score: 40\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.8, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 63, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: 40\n - Market penetration index: 51\n - Demographic fit rating: 34\n - Competitive landscape score: 40\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.8, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 63, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: 40\n - Market penetration index: 51\n - Demographic fit rating: 34\n - Competitive landscape score: 40\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.8, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 63, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: 40\n - Market penetration index: 51\n - Demographic fit rating: 34\n - Competitive landscape score: 40\n - Brand awareness index: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.8, \"count\": 5, \"min\": 34.0, \"max\": 51.0, \"std\": 5.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 63, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: 52\n - Channel readiness index: 49\n - Price-value perception rating: 47\n - Referral likelihood score: 60\n - Category awareness rating: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.4, \"count\": 5, \"min\": 47.0, \"max\": 60.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: 52\n - Channel readiness index: 49\n - Price-value perception rating: 47\n - Referral likelihood score: 60\n - Category awareness rating: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.4, \"count\": 5, \"min\": 47.0, \"max\": 60.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: 52\n - Channel readiness index: 49\n - Price-value perception rating: 47\n - Referral likelihood score: 60\n - Category awareness rating: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.4, \"count\": 5, \"min\": 47.0, \"max\": 60.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: 52\n - Channel readiness index: 49\n - Price-value perception rating: 47\n - Referral likelihood score: 60\n - Category awareness rating: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.4, \"count\": 5, \"min\": 47.0, \"max\": 60.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: 52\n - Channel readiness index: 49\n - Price-value perception rating: 47\n - Referral likelihood score: 60\n - Category awareness rating: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.4, \"count\": 5, \"min\": 47.0, \"max\": 60.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: 39\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 43\n - Geographic coverage score: 46\n - Customer lifetime value predictor: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 39.0, \"max\": 59.0, \"std\": 6.83}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: 39\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 43\n - Geographic coverage score: 46\n - Customer lifetime value predictor: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 39.0, \"max\": 59.0, \"std\": 6.83}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 73, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: 39\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 43\n - Geographic coverage score: 46\n - Customer lifetime value predictor: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 39.0, \"max\": 59.0, \"std\": 6.83}\n\nTool: check_external_reference\nOutput: {\"request_id\": 73, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: 39\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 43\n - Geographic coverage score: 46\n - Customer lifetime value predictor: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 39.0, \"max\": 59.0, \"std\": 6.83}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 73, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: 39\n - Social media sentiment index: 59\n - Retail shelf readiness rating: 43\n - Geographic coverage score: 46\n - Customer lifetime value predictor: 50\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.4, \"count\": 5, \"min\": 39.0, \"max\": 59.0, \"std\": 6.83}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 73, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 78\n - Demographic fit rating: 70\n - Competitive landscape score: 75\n - Brand awareness index: 82\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 73.6, \"count\": 5, \"min\": 63.0, \"max\": 82.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 74, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 78\n - Demographic fit rating: 70\n - Competitive landscape score: 75\n - Brand awareness index: 82\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 73.6, \"count\": 5, \"min\": 63.0, \"max\": 82.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 74, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 78\n - Demographic fit rating: 70\n - Competitive landscape score: 75\n - Brand awareness index: 82\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 73.6, \"count\": 5, \"min\": 63.0, \"max\": 82.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"request_id\": 94, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 74, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 78\n - Demographic fit rating: 70\n - Competitive landscape score: 75\n - Brand awareness index: 82\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 73.6, \"count\": 5, \"min\": 63.0, \"max\": 82.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 74, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 78\n - Demographic fit rating: 70\n - Competitive landscape score: 75\n - Brand awareness index: 82\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 73.6, \"count\": 5, \"min\": 63.0, \"max\": 82.0, \"std\": 6.59}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 94, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 74, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: 46\n - Price-value perception rating: 33\n - Referral likelihood score: 51\n - Category awareness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.4, \"count\": 5, \"min\": 33.0, \"max\": 51.0, \"std\": 6.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: 46\n - Price-value perception rating: 33\n - Referral likelihood score: 51\n - Category awareness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.4, \"count\": 5, \"min\": 33.0, \"max\": 51.0, \"std\": 6.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: 46\n - Price-value perception rating: 33\n - Referral likelihood score: 51\n - Category awareness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.4, \"count\": 5, \"min\": 33.0, \"max\": 51.0, \"std\": 6.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: 46\n - Price-value perception rating: 33\n - Referral likelihood score: 51\n - Category awareness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.4, \"count\": 5, \"min\": 33.0, \"max\": 51.0, \"std\": 6.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: 46\n - Price-value perception rating: 33\n - Referral likelihood score: 51\n - Category awareness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.4, \"count\": 5, \"min\": 33.0, \"max\": 51.0, \"std\": 6.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: 36\n - Social media sentiment index: 37\n - Retail shelf readiness rating: 18\n - Geographic coverage score: 45\n - Customer lifetime value predictor: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 18.0, \"max\": 45.0, \"std\": 8.87}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 14, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: 36\n - Social media sentiment index: 37\n - Retail shelf readiness rating: 18\n - Geographic coverage score: 45\n - Customer lifetime value predictor: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 18.0, \"max\": 45.0, \"std\": 8.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 14, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 64, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: 36\n - Social media sentiment index: 37\n - Retail shelf readiness rating: 18\n - Geographic coverage score: 45\n - Customer lifetime value predictor: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 18.0, \"max\": 45.0, \"std\": 8.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 64, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 14, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: 36\n - Social media sentiment index: 37\n - Retail shelf readiness rating: 18\n - Geographic coverage score: 45\n - Customer lifetime value predictor: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 18.0, \"max\": 45.0, \"std\": 8.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 14, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-market_demographics-e-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 64, "offset": 25, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: 36\n - Social media sentiment index: 37\n - Retail shelf readiness rating: 18\n - Geographic coverage score: 45\n - Customer lifetime value predictor: 32\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 33.6, \"count\": 5, \"min\": 18.0, \"max\": 45.0, \"std\": 8.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 64, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 34, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 65\n - Demographic fit rating: 64\n - Competitive landscape score: 73\n - Brand awareness index: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 54.0, \"max\": 73.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 65\n - Demographic fit rating: 64\n - Competitive landscape score: 73\n - Brand awareness index: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 54.0, \"max\": 73.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 65\n - Demographic fit rating: 64\n - Competitive landscape score: 73\n - Brand awareness index: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 54.0, \"max\": 73.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 65\n - Demographic fit rating: 64\n - Competitive landscape score: 73\n - Brand awareness index: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 54.0, \"max\": 73.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 63\n - Market penetration index: 65\n - Demographic fit rating: 64\n - Competitive landscape score: 73\n - Brand awareness index: 54\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.8, \"count\": 5, \"min\": 54.0, \"max\": 73.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 66\n - Channel readiness index: 69\n - Price-value perception rating: 64\n - Referral likelihood score: 75\n - Category awareness rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.4, \"count\": 5, \"min\": 64.0, \"max\": 75.0, \"std\": 4.13}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 28, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 66\n - Channel readiness index: 69\n - Price-value perception rating: 64\n - Referral likelihood score: 75\n - Category awareness rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.4, \"count\": 5, \"min\": 64.0, \"max\": 75.0, \"std\": 4.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 28, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 66\n - Channel readiness index: 69\n - Price-value perception rating: 64\n - Referral likelihood score: 75\n - Category awareness rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.4, \"count\": 5, \"min\": 64.0, \"max\": 75.0, \"std\": 4.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 28, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 66\n - Channel readiness index: 69\n - Price-value perception rating: 64\n - Referral likelihood score: 75\n - Category awareness rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.4, \"count\": 5, \"min\": 64.0, \"max\": 75.0, \"std\": 4.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 28, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 66\n - Channel readiness index: 69\n - Price-value perception rating: 64\n - Referral likelihood score: 75\n - Category awareness rating: 73\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.4, \"count\": 5, \"min\": 64.0, \"max\": 75.0, \"std\": 4.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: 31\n - Social media sentiment index: 45\n - Retail shelf readiness rating: 42\n - Geographic coverage score: 29\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 29.0, \"max\": 45.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: 31\n - Social media sentiment index: 45\n - Retail shelf readiness rating: 42\n - Geographic coverage score: 29\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 29.0, \"max\": 45.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: 31\n - Social media sentiment index: 45\n - Retail shelf readiness rating: 42\n - Geographic coverage score: 29\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 29.0, \"max\": 45.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: 31\n - Social media sentiment index: 45\n - Retail shelf readiness rating: 42\n - Geographic coverage score: 29\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 29.0, \"max\": 45.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: 31\n - Social media sentiment index: 45\n - Retail shelf readiness rating: 42\n - Geographic coverage score: 29\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 35.6, \"count\": 5, \"min\": 29.0, \"max\": 45.0, \"std\": 6.56}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 52\n - Market penetration index: 51\n - Demographic fit rating: 56\n - Competitive landscape score: 50\n - Brand awareness index: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 50.0, \"max\": 56.0, \"std\": 2.1}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 12, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 52\n - Market penetration index: 51\n - Demographic fit rating: 56\n - Competitive landscape score: 50\n - Brand awareness index: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 50.0, \"max\": 56.0, \"std\": 2.1}\n\nTool: check_external_reference\nOutput: {\"request_id\": 12, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 92, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 52\n - Market penetration index: 51\n - Demographic fit rating: 56\n - Competitive landscape score: 50\n - Brand awareness index: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 50.0, \"max\": 56.0, \"std\": 2.1}\n\nTool: check_external_reference\nOutput: {\"request_id\": 92, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 12, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 52\n - Market penetration index: 51\n - Demographic fit rating: 56\n - Competitive landscape score: 50\n - Brand awareness index: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 50.0, \"max\": 56.0, \"std\": 2.1}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 12, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 92, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 52\n - Market penetration index: 51\n - Demographic fit rating: 56\n - Competitive landscape score: 50\n - Brand awareness index: 51\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 5, \"min\": 50.0, \"max\": 56.0, \"std\": 2.1}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 92, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 74\n - Channel readiness index: 62\n - Price-value perception rating: 59\n - Referral likelihood score: 54\n - Category awareness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 54.0, \"max\": 74.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 74\n - Channel readiness index: 62\n - Price-value perception rating: 59\n - Referral likelihood score: 54\n - Category awareness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 54.0, \"max\": 74.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 74\n - Channel readiness index: 62\n - Price-value perception rating: 59\n - Referral likelihood score: 54\n - Category awareness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 54.0, \"max\": 74.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 74\n - Channel readiness index: 62\n - Price-value perception rating: 59\n - Referral likelihood score: 54\n - Category awareness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 54.0, \"max\": 74.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 74\n - Channel readiness index: 62\n - Price-value perception rating: 59\n - Referral likelihood score: 54\n - Category awareness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 54.0, \"max\": 74.0, \"std\": 6.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 61\n - Social media sentiment index: 64\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 64\n - Customer lifetime value predictor: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 47.0, \"max\": 68.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 61\n - Social media sentiment index: 64\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 64\n - Customer lifetime value predictor: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 47.0, \"max\": 68.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 61\n - Social media sentiment index: 64\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 64\n - Customer lifetime value predictor: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 47.0, \"max\": 68.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 61\n - Social media sentiment index: 64\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 64\n - Customer lifetime value predictor: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 47.0, \"max\": 68.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 61\n - Social media sentiment index: 64\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 64\n - Customer lifetime value predictor: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.8, \"count\": 5, \"min\": 47.0, \"max\": 68.0, \"std\": 7.25}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 68\n - Market penetration index: 68\n - Demographic fit rating: 66\n - Competitive landscape score: 62\n - Brand awareness index: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 66.0, \"count\": 5, \"min\": 62.0, \"max\": 68.0, \"std\": 2.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 66, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 68\n - Market penetration index: 68\n - Demographic fit rating: 66\n - Competitive landscape score: 62\n - Brand awareness index: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 66.0, \"count\": 5, \"min\": 62.0, \"max\": 68.0, \"std\": 2.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 66, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 68\n - Market penetration index: 68\n - Demographic fit rating: 66\n - Competitive landscape score: 62\n - Brand awareness index: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 66.0, \"count\": 5, \"min\": 62.0, \"max\": 68.0, \"std\": 2.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 66, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 68\n - Market penetration index: 68\n - Demographic fit rating: 66\n - Competitive landscape score: 62\n - Brand awareness index: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 66.0, \"count\": 5, \"min\": 62.0, \"max\": 68.0, \"std\": 2.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 66, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 68\n - Market penetration index: 68\n - Demographic fit rating: 66\n - Competitive landscape score: 62\n - Brand awareness index: 66\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 66.0, \"count\": 5, \"min\": 62.0, \"max\": 68.0, \"std\": 2.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 66, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 42\n - Channel readiness index: 49\n - Price-value perception rating: 45\n - Referral likelihood score: 41\n - Category awareness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 39.0, \"max\": 49.0, \"std\": 3.49}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 4, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 42\n - Channel readiness index: 49\n - Price-value perception rating: 45\n - Referral likelihood score: 41\n - Category awareness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 39.0, \"max\": 49.0, \"std\": 3.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 4, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 42\n - Channel readiness index: 49\n - Price-value perception rating: 45\n - Referral likelihood score: 41\n - Category awareness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 39.0, \"max\": 49.0, \"std\": 3.49}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 4, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 42\n - Channel readiness index: 49\n - Price-value perception rating: 45\n - Referral likelihood score: 41\n - Category awareness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 39.0, \"max\": 49.0, \"std\": 3.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 4, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 42\n - Channel readiness index: 49\n - Price-value perception rating: 45\n - Referral likelihood score: 41\n - Category awareness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.2, \"count\": 5, \"min\": 39.0, \"max\": 49.0, \"std\": 3.49}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 40\n - Social media sentiment index: 46\n - Retail shelf readiness rating: 45\n - Geographic coverage score: 43\n - Customer lifetime value predictor: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 22.0, \"max\": 46.0, \"std\": 8.84}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 40\n - Social media sentiment index: 46\n - Retail shelf readiness rating: 45\n - Geographic coverage score: 43\n - Customer lifetime value predictor: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 22.0, \"max\": 46.0, \"std\": 8.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 40\n - Social media sentiment index: 46\n - Retail shelf readiness rating: 45\n - Geographic coverage score: 43\n - Customer lifetime value predictor: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 22.0, \"max\": 46.0, \"std\": 8.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 40\n - Social media sentiment index: 46\n - Retail shelf readiness rating: 45\n - Geographic coverage score: 43\n - Customer lifetime value predictor: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 22.0, \"max\": 46.0, \"std\": 8.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: 40\n - Social media sentiment index: 46\n - Retail shelf readiness rating: 45\n - Geographic coverage score: 43\n - Customer lifetime value predictor: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.2, \"count\": 5, \"min\": 22.0, \"max\": 46.0, \"std\": 8.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 33\n - Market penetration index: 32\n - Demographic fit rating: 40\n - Competitive landscape score: 48\n - Brand awareness index: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 32.0, \"max\": 49.0, \"std\": 7.17}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 33\n - Market penetration index: 32\n - Demographic fit rating: 40\n - Competitive landscape score: 48\n - Brand awareness index: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 32.0, \"max\": 49.0, \"std\": 7.17}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 33\n - Market penetration index: 32\n - Demographic fit rating: 40\n - Competitive landscape score: 48\n - Brand awareness index: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 32.0, \"max\": 49.0, \"std\": 7.17}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 33\n - Market penetration index: 32\n - Demographic fit rating: 40\n - Competitive landscape score: 48\n - Brand awareness index: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 32.0, \"max\": 49.0, \"std\": 7.17}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-e-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 40, "difficulty": "easy", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: 33\n - Market penetration index: 32\n - Demographic fit rating: 40\n - Competitive landscape score: 48\n - Brand awareness index: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 40.4, \"count\": 5, \"min\": 32.0, \"max\": 49.0, \"std\": 7.17}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 67\n - Price-value perception rating: 54\n - Referral likelihood score: [data not available]\n - Category awareness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 35.0, \"max\": 67.0, \"std\": 13.14}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 41, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 67\n - Price-value perception rating: 54\n - Referral likelihood score: [data not available]\n - Category awareness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 35.0, \"max\": 67.0, \"std\": 13.14}\n\nTool: check_external_reference\nOutput: {\"request_id\": 41, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 71, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 67\n - Price-value perception rating: 54\n - Referral likelihood score: [data not available]\n - Category awareness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 35.0, \"max\": 67.0, \"std\": 13.14}\n\nTool: check_external_reference\nOutput: {\"request_id\": 71, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 41, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 67\n - Price-value perception rating: 54\n - Referral likelihood score: [data not available]\n - Category awareness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 35.0, \"max\": 67.0, \"std\": 13.14}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 41, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 71, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 67\n - Price-value perception rating: 54\n - Referral likelihood score: [data not available]\n - Category awareness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 35.0, \"max\": 67.0, \"std\": 13.14}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 71, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 62\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 71\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 40.0, \"max\": 71.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 51, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 62\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 71\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 40.0, \"max\": 71.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"request_id\": 51, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 62\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 71\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 40.0, \"max\": 71.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 51, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 62\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 71\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 40.0, \"max\": 71.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 51, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: 62\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 71\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 40.0, \"max\": 71.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 43\n - Market penetration index: [data not available]\n - Demographic fit rating: 74\n - Competitive landscape score: [data not available]\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 31.0, \"max\": 74.0, \"std\": 18.12}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 43\n - Market penetration index: [data not available]\n - Demographic fit rating: 74\n - Competitive landscape score: [data not available]\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 31.0, \"max\": 74.0, \"std\": 18.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 43\n - Market penetration index: [data not available]\n - Demographic fit rating: 74\n - Competitive landscape score: [data not available]\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 31.0, \"max\": 74.0, \"std\": 18.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 50, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 43\n - Market penetration index: [data not available]\n - Demographic fit rating: 74\n - Competitive landscape score: [data not available]\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 31.0, \"max\": 74.0, \"std\": 18.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 50, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: 43\n - Market penetration index: [data not available]\n - Demographic fit rating: 74\n - Competitive landscape score: [data not available]\n - Brand awareness index: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 31.0, \"max\": 74.0, \"std\": 18.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 50, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 47\n - Channel readiness index: [data not available]\n - Price-value perception rating: 54\n - Referral likelihood score: 34\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 34.0, \"max\": 54.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 47\n - Channel readiness index: [data not available]\n - Price-value perception rating: 54\n - Referral likelihood score: 34\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 34.0, \"max\": 54.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 51, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 47\n - Channel readiness index: [data not available]\n - Price-value perception rating: 54\n - Referral likelihood score: 34\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 34.0, \"max\": 54.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"request_id\": 51, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 47\n - Channel readiness index: [data not available]\n - Price-value perception rating: 54\n - Referral likelihood score: 34\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 34.0, \"max\": 54.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 51, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 47\n - Channel readiness index: [data not available]\n - Price-value perception rating: 54\n - Referral likelihood score: 34\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 3, \"min\": 34.0, \"max\": 54.0, \"std\": 8.29}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 51, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 57\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 47\n - Customer lifetime value predictor: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 47.0, \"max\": 57.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 57\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 47\n - Customer lifetime value predictor: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 47.0, \"max\": 57.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 47, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 77, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 57\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 47\n - Customer lifetime value predictor: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 47.0, \"max\": 57.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"request_id\": 77, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 47, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 57\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 47\n - Customer lifetime value predictor: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 47.0, \"max\": 57.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 47, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 77, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 57\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 47\n - Customer lifetime value predictor: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.33, \"count\": 3, \"min\": 47.0, \"max\": 57.0, \"std\": 4.5}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 77, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 77\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 69\n - Brand awareness index: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.33, \"count\": 3, \"min\": 47.0, \"max\": 77.0, \"std\": 12.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 77\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 69\n - Brand awareness index: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.33, \"count\": 3, \"min\": 47.0, \"max\": 77.0, \"std\": 12.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 54, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 77\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 69\n - Brand awareness index: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.33, \"count\": 3, \"min\": 47.0, \"max\": 77.0, \"std\": 12.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 54, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 77\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 69\n - Brand awareness index: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.33, \"count\": 3, \"min\": 47.0, \"max\": 77.0, \"std\": 12.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 54, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 77\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 69\n - Brand awareness index: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.33, \"count\": 3, \"min\": 47.0, \"max\": 77.0, \"std\": 12.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 54, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 57\n - Price-value perception rating: 34\n - Referral likelihood score: 54\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 34.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 57\n - Price-value perception rating: 34\n - Referral likelihood score: 54\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 34.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 57\n - Price-value perception rating: 34\n - Referral likelihood score: 54\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 34.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 57\n - Price-value perception rating: 34\n - Referral likelihood score: 54\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 34.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 57\n - Price-value perception rating: 34\n - Referral likelihood score: 54\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.33, \"count\": 3, \"min\": 34.0, \"max\": 57.0, \"std\": 10.21}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 55\n - Social media sentiment index: 58\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 38\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 38.0, \"max\": 58.0, \"std\": 8.81}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 55\n - Social media sentiment index: 58\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 38\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 38.0, \"max\": 58.0, \"std\": 8.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 38, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 55\n - Social media sentiment index: 58\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 38\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 38.0, \"max\": 58.0, \"std\": 8.81}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 55\n - Social media sentiment index: 58\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 38\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 38.0, \"max\": 58.0, \"std\": 8.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 38, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Trial conversion score: 55\n - Social media sentiment index: 58\n - Retail shelf readiness rating: [data not available]\n - Geographic coverage score: 38\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.33, \"count\": 3, \"min\": 38.0, \"max\": 58.0, \"std\": 8.81}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 43\n - Demographic fit rating: 100\n - Competitive landscape score: 61\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 43.0, \"max\": 100.0, \"std\": 23.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 43\n - Demographic fit rating: 100\n - Competitive landscape score: 61\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 43.0, \"max\": 100.0, \"std\": 23.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 38, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 43\n - Demographic fit rating: 100\n - Competitive landscape score: 61\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 43.0, \"max\": 100.0, \"std\": 23.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 43\n - Demographic fit rating: 100\n - Competitive landscape score: 61\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 43.0, \"max\": 100.0, \"std\": 23.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 38, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 43\n - Demographic fit rating: 100\n - Competitive landscape score: 61\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.0, \"count\": 3, \"min\": 43.0, \"max\": 100.0, \"std\": 23.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 66\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 51\n - Category awareness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 51.0, \"max\": 74.0, \"std\": 9.53}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 66\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 51\n - Category awareness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 51.0, \"max\": 74.0, \"std\": 9.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 38, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 66\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 51\n - Category awareness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 51.0, \"max\": 74.0, \"std\": 9.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 68, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 38, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 66\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 51\n - Category awareness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 51.0, \"max\": 74.0, \"std\": 9.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 38, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 68, "offset": 15, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 66\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 51\n - Category awareness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 51.0, \"max\": 74.0, \"std\": 9.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 68, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 19\n - Geographic coverage score: 75\n - Customer lifetime value predictor: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 19.0, \"max\": 75.0, \"std\": 23.55}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 19\n - Geographic coverage score: 75\n - Customer lifetime value predictor: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 19.0, \"max\": 75.0, \"std\": 23.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 19\n - Geographic coverage score: 75\n - Customer lifetime value predictor: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 19.0, \"max\": 75.0, \"std\": 23.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 19\n - Geographic coverage score: 75\n - Customer lifetime value predictor: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 19.0, \"max\": 75.0, \"std\": 23.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 19\n - Geographic coverage score: 75\n - Customer lifetime value predictor: 59\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.0, \"count\": 3, \"min\": 19.0, \"max\": 75.0, \"std\": 23.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 48\n - Market penetration index: [data not available]\n - Demographic fit rating: 60\n - Competitive landscape score: [data not available]\n - Brand awareness index: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 44.0, \"max\": 60.0, \"std\": 6.8}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 48\n - Market penetration index: [data not available]\n - Demographic fit rating: 60\n - Competitive landscape score: [data not available]\n - Brand awareness index: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 44.0, \"max\": 60.0, \"std\": 6.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 48\n - Market penetration index: [data not available]\n - Demographic fit rating: 60\n - Competitive landscape score: [data not available]\n - Brand awareness index: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 44.0, \"max\": 60.0, \"std\": 6.8}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 48\n - Market penetration index: [data not available]\n - Demographic fit rating: 60\n - Competitive landscape score: [data not available]\n - Brand awareness index: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 44.0, \"max\": 60.0, \"std\": 6.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Early adopter survey score: 48\n - Market penetration index: [data not available]\n - Demographic fit rating: 60\n - Competitive landscape score: [data not available]\n - Brand awareness index: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.67, \"count\": 3, \"min\": 44.0, \"max\": 60.0, \"std\": 6.8}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 73\n - Price-value perception rating: 9\n - Referral likelihood score: 60\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 9.0, \"max\": 73.0, \"std\": 27.62}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 45, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 73\n - Price-value perception rating: 9\n - Referral likelihood score: 60\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 9.0, \"max\": 73.0, \"std\": 27.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 95, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 73\n - Price-value perception rating: 9\n - Referral likelihood score: 60\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 9.0, \"max\": 73.0, \"std\": 27.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 95, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 45, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 73\n - Price-value perception rating: 9\n - Referral likelihood score: 60\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 9.0, \"max\": 73.0, \"std\": 27.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 95, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: 73\n - Price-value perception rating: 9\n - Referral likelihood score: 60\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 9.0, \"max\": 73.0, \"std\": 27.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 95, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 67\n - Geographic coverage score: 28\n - Customer lifetime value predictor: 81\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.67, \"count\": 3, \"min\": 28.0, \"max\": 81.0, \"std\": 22.43}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 67\n - Geographic coverage score: 28\n - Customer lifetime value predictor: 81\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.67, \"count\": 3, \"min\": 28.0, \"max\": 81.0, \"std\": 22.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 67\n - Geographic coverage score: 28\n - Customer lifetime value predictor: 81\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.67, \"count\": 3, \"min\": 28.0, \"max\": 81.0, \"std\": 22.43}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 67\n - Geographic coverage score: 28\n - Customer lifetime value predictor: 81\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.67, \"count\": 3, \"min\": 28.0, \"max\": 81.0, \"std\": 22.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 67\n - Geographic coverage score: 28\n - Customer lifetime value predictor: 81\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.67, \"count\": 3, \"min\": 28.0, \"max\": 81.0, \"std\": 22.43}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 59, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 75\n - Market penetration index: 45\n - Demographic fit rating: 18\n - Competitive landscape score: [data not available]\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 3, \"min\": 18.0, \"max\": 75.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 7, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 75\n - Market penetration index: 45\n - Demographic fit rating: 18\n - Competitive landscape score: [data not available]\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 3, \"min\": 18.0, \"max\": 75.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 7, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 57, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 75\n - Market penetration index: 45\n - Demographic fit rating: 18\n - Competitive landscape score: [data not available]\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 3, \"min\": 18.0, \"max\": 75.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 57, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 7, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 75\n - Market penetration index: 45\n - Demographic fit rating: 18\n - Competitive landscape score: [data not available]\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 3, \"min\": 18.0, \"max\": 75.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 7, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 57, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Early adopter survey score: 75\n - Market penetration index: 45\n - Demographic fit rating: 18\n - Competitive landscape score: [data not available]\n - Brand awareness index: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 3, \"min\": 18.0, \"max\": 75.0, \"std\": 23.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 57, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: [data not available]\n - Price-value perception rating: 62\n - Referral likelihood score: [data not available]\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 40.0, \"max\": 68.0, \"std\": 12.04}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: [data not available]\n - Price-value perception rating: 62\n - Referral likelihood score: [data not available]\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 40.0, \"max\": 68.0, \"std\": 12.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 35, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: [data not available]\n - Price-value perception rating: 62\n - Referral likelihood score: [data not available]\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 40.0, \"max\": 68.0, \"std\": 12.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: [data not available]\n - Price-value perception rating: 62\n - Referral likelihood score: [data not available]\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 40.0, \"max\": 68.0, \"std\": 12.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 35, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: [data not available]\n - Price-value perception rating: 62\n - Referral likelihood score: [data not available]\n - Category awareness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 40.0, \"max\": 68.0, \"std\": 12.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 50\n - Geographic coverage score: 66\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 31.0, \"max\": 66.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 50\n - Geographic coverage score: 66\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 31.0, \"max\": 66.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 50\n - Geographic coverage score: 66\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 31.0, \"max\": 66.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 50\n - Geographic coverage score: 66\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 31.0, \"max\": 66.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 50\n - Geographic coverage score: 66\n - Customer lifetime value predictor: 31\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.0, \"count\": 3, \"min\": 31.0, \"max\": 66.0, \"std\": 14.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 43\n - Brand awareness index: 79\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 43.0, \"max\": 79.0, \"std\": 15.17}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 43\n - Brand awareness index: 79\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 43.0, \"max\": 79.0, \"std\": 15.17}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 43\n - Brand awareness index: 79\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 43.0, \"max\": 79.0, \"std\": 15.17}\n\nTool: check_external_reference\nOutput: {\"request_id\": 70, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 43\n - Brand awareness index: 79\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 43.0, \"max\": 79.0, \"std\": 15.17}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 70, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Early adopter survey score: 53\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 43\n - Brand awareness index: 79\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 58.33, \"count\": 3, \"min\": 43.0, \"max\": 79.0, \"std\": 15.17}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 70, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: 54\n - Price-value perception rating: [data not available]\n - Referral likelihood score: [data not available]\n - Category awareness rating: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.33, \"count\": 3, \"min\": 54.0, \"max\": 68.0, \"std\": 5.73}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 5, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: 54\n - Price-value perception rating: [data not available]\n - Referral likelihood score: [data not available]\n - Category awareness rating: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.33, \"count\": 3, \"min\": 54.0, \"max\": 68.0, \"std\": 5.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 5, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 55, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: 54\n - Price-value perception rating: [data not available]\n - Referral likelihood score: [data not available]\n - Category awareness rating: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.33, \"count\": 3, \"min\": 54.0, \"max\": 68.0, \"std\": 5.73}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 5, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: 54\n - Price-value perception rating: [data not available]\n - Referral likelihood score: [data not available]\n - Category awareness rating: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.33, \"count\": 3, \"min\": 54.0, \"max\": 68.0, \"std\": 5.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 5, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 55, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Purchase intent score: 68\n - Channel readiness index: 54\n - Price-value perception rating: [data not available]\n - Referral likelihood score: [data not available]\n - Category awareness rating: 62\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.33, \"count\": 3, \"min\": 54.0, \"max\": 68.0, \"std\": 5.73}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 53\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 27\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 27.0, \"max\": 53.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 45, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 53\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 27\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 27.0, \"max\": 53.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 95, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 53\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 27\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 27.0, \"max\": 53.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"request_id\": 95, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 45, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 53\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 27\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 27.0, \"max\": 53.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-market_demographics-h-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 95, "offset": 25, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Trial conversion score: 53\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 47\n - Geographic coverage score: 27\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.33, \"count\": 3, \"min\": 27.0, \"max\": 53.0, \"std\": 11.12}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 95, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 87\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 0\n - Brand awareness index: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 0.0, \"max\": 87.0, \"std\": 35.69}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 87\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 0\n - Brand awareness index: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 0.0, \"max\": 87.0, \"std\": 35.69}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 71, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 87\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 0\n - Brand awareness index: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 0.0, \"max\": 87.0, \"std\": 35.69}\n\nTool: check_external_reference\nOutput: {\"request_id\": 71, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 87\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 0\n - Brand awareness index: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 0.0, \"max\": 87.0, \"std\": 35.69}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 71, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Early adopter survey score: 87\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 0\n - Brand awareness index: 36\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 0.0, \"max\": 87.0, \"std\": 35.69}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 71, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 54\n - Channel readiness index: [data not available]\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 6\n - Category awareness rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 6.0, \"max\": 56.0, \"std\": 23.11}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 54\n - Channel readiness index: [data not available]\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 6\n - Category awareness rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 6.0, \"max\": 56.0, \"std\": 23.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 27, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 54\n - Channel readiness index: [data not available]\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 6\n - Category awareness rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 6.0, \"max\": 56.0, \"std\": 23.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 27, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 54\n - Channel readiness index: [data not available]\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 6\n - Category awareness rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 6.0, \"max\": 56.0, \"std\": 23.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 27, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Purchase intent score: 54\n - Channel readiness index: [data not available]\n - Price-value perception rating: [data not available]\n - Referral likelihood score: 6\n - Category awareness rating: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 6.0, \"max\": 56.0, \"std\": 23.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 64\n - Geographic coverage score: 34\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 34.0, \"max\": 64.0, \"std\": 12.28}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 64\n - Geographic coverage score: 34\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 34.0, \"max\": 64.0, \"std\": 12.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 1, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 64\n - Geographic coverage score: 34\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 34.0, \"max\": 64.0, \"std\": 12.28}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 64\n - Geographic coverage score: 34\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 34.0, \"max\": 64.0, \"std\": 12.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 1, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA sustainable fashion brand is assessing market readiness for a new product line. Survey scores are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 51\n - Retail shelf readiness rating: 64\n - Geographic coverage score: 34\n - Customer lifetime value predictor: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 34.0, \"max\": 64.0, \"std\": 12.28}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 53\n - Demographic fit rating: 26\n - Competitive landscape score: [data not available]\n - Brand awareness index: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.33, \"count\": 3, \"min\": 26.0, \"max\": 57.0, \"std\": 13.77}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 53\n - Demographic fit rating: 26\n - Competitive landscape score: [data not available]\n - Brand awareness index: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.33, \"count\": 3, \"min\": 26.0, \"max\": 57.0, \"std\": 13.77}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 53\n - Demographic fit rating: 26\n - Competitive landscape score: [data not available]\n - Brand awareness index: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.33, \"count\": 3, \"min\": 26.0, \"max\": 57.0, \"std\": 13.77}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 53\n - Demographic fit rating: 26\n - Competitive landscape score: [data not available]\n - Brand awareness index: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.33, \"count\": 3, \"min\": 26.0, \"max\": 57.0, \"std\": 13.77}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA food delivery service is estimating adoption potential in a new metropolitan area. Five market indicators were collected.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 53\n - Demographic fit rating: 26\n - Competitive landscape score: [data not available]\n - Brand awareness index: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.33, \"count\": 3, \"min\": 26.0, \"max\": 57.0, \"std\": 13.77}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: [data not available]\n - Price-value perception rating: 72\n - Referral likelihood score: 52\n - Category awareness rating: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 22.0, \"max\": 72.0, \"std\": 20.55}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: [data not available]\n - Price-value perception rating: 72\n - Referral likelihood score: 52\n - Category awareness rating: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 22.0, \"max\": 72.0, \"std\": 20.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: [data not available]\n - Price-value perception rating: 72\n - Referral likelihood score: 52\n - Category awareness rating: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 22.0, \"max\": 72.0, \"std\": 20.55}\n\nTool: check_external_reference\nOutput: {\"request_id\": 78, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: [data not available]\n - Price-value perception rating: 72\n - Referral likelihood score: 52\n - Category awareness rating: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 22.0, \"max\": 72.0, \"std\": 20.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 78, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fitness technology company is evaluating demand for a wearable health device. Research scores are as follows.\n\nEvidence:\n - Purchase intent score: [data not available]\n - Channel readiness index: [data not available]\n - Price-value perception rating: 72\n - Referral likelihood score: 52\n - Category awareness rating: 22\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 22.0, \"max\": 72.0, \"std\": 20.55}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 78, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 33\n - Geographic coverage score: 50\n - Customer lifetime value predictor: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 3, \"min\": 33.0, \"max\": 50.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 33\n - Geographic coverage score: 50\n - Customer lifetime value predictor: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 3, \"min\": 33.0, \"max\": 50.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 7, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 33\n - Geographic coverage score: 50\n - Customer lifetime value predictor: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 3, \"min\": 33.0, \"max\": 50.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 87, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 33\n - Geographic coverage score: 50\n - Customer lifetime value predictor: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 3, \"min\": 33.0, \"max\": 50.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 7, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA startup is evaluating the adoption potential for a new mobile app in a target demographic. Five market research signals are summarized below.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: [data not available]\n - Retail shelf readiness rating: 33\n - Geographic coverage score: 50\n - Customer lifetime value predictor: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.0, \"count\": 3, \"min\": 33.0, \"max\": 50.0, \"std\": 7.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 87, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 56\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 64\n - Brand awareness index: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 56.0, \"max\": 71.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 56\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 64\n - Brand awareness index: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 56.0, \"max\": 71.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 56\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 64\n - Brand awareness index: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 56.0, \"max\": 71.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 56\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 64\n - Brand awareness index: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 56.0, \"max\": 71.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA consumer electronics company is assessing product-market fit. Independent research scores (0–100) are as follows.\n\nEvidence:\n - Early adopter survey score: [data not available]\n - Market penetration index: 56\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 64\n - Brand awareness index: 71\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.67, \"count\": 3, \"min\": 56.0, \"max\": 71.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the market data above, what is your best estimate for the adoption index (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 64, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: [data not available]\n - Price-value perception rating: 61\n - Referral likelihood score: 81\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.67, \"count\": 3, \"min\": 43.0, \"max\": 81.0, \"std\": 15.52}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 23, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: [data not available]\n - Price-value perception rating: 61\n - Referral likelihood score: 81\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.67, \"count\": 3, \"min\": 43.0, \"max\": 81.0, \"std\": 15.52}\n\nTool: check_external_reference\nOutput: {\"request_id\": 23, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: [data not available]\n - Price-value perception rating: 61\n - Referral likelihood score: 81\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.67, \"count\": 3, \"min\": 43.0, \"max\": 81.0, \"std\": 15.52}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 23, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: [data not available]\n - Price-value perception rating: 61\n - Referral likelihood score: 81\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.67, \"count\": 3, \"min\": 43.0, \"max\": 81.0, \"std\": 15.52}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 23, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA fintech firm is gauging adoption likelihood for a digital banking product. Five survey-based indicators are available.\n\nEvidence:\n - Purchase intent score: 43\n - Channel readiness index: [data not available]\n - Price-value perception rating: 61\n - Referral likelihood score: 81\n - Category awareness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.67, \"count\": 3, \"min\": 43.0, \"max\": 81.0, \"std\": 15.52}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite market adoption score (0–100) from the indicators provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 44\n - Retail shelf readiness rating: 50\n - Geographic coverage score: [data not available]\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 40.0, \"max\": 50.0, \"std\": 4.11}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 4, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 44\n - Retail shelf readiness rating: 50\n - Geographic coverage score: [data not available]\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 40.0, \"max\": 50.0, \"std\": 4.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 4, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 44\n - Retail shelf readiness rating: 50\n - Geographic coverage score: [data not available]\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 40.0, \"max\": 50.0, \"std\": 4.11}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 4, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 44\n - Retail shelf readiness rating: 50\n - Geographic coverage score: [data not available]\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 40.0, \"max\": 50.0, \"std\": 4.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 4, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA health-tech company is estimating patient adoption potential for a new telehealth platform. Market research scores are summarized.\n\nEvidence:\n - Trial conversion score: [data not available]\n - Social media sentiment index: 44\n - Retail shelf readiness rating: 50\n - Geographic coverage score: [data not available]\n - Customer lifetime value predictor: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 40.0, \"max\": 50.0, \"std\": 4.11}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall adoption potential index (0–100) do you estimate based on these signals?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 46\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 24\n - Brand awareness index: 17\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 17.0, \"max\": 46.0, \"std\": 12.36}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 46\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 24\n - Brand awareness index: 17\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 17.0, \"max\": 46.0, \"std\": 12.36}\n\nTool: check_external_reference\nOutput: {\"request_id\": 0, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 79, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 46\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 24\n - Brand awareness index: 17\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 17.0, \"max\": 46.0, \"std\": 12.36}\n\nTool: check_external_reference\nOutput: {\"request_id\": 79, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 0, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 46\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 24\n - Brand awareness index: 17\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 17.0, \"max\": 46.0, \"std\": 12.36}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 0, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-market_demographics-h-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 79, "offset": 40, "difficulty": "hard", "domain": "market_demographics", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn edtech startup is evaluating adoption potential for an AI tutoring platform. Five research signals are available.\n\nEvidence:\n - Early adopter survey score: 46\n - Market penetration index: [data not available]\n - Demographic fit rating: [data not available]\n - Competitive landscape score: 24\n - Brand awareness index: 17\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 29.0, \"count\": 3, \"min\": 17.0, \"max\": 46.0, \"std\": 12.36}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 79, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these research signals, estimate the overall market adoption potential (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 29, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 54\n - Incident response rating: 43\n - Documentation compliance score: 62\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 43.0, \"max\": 70.0, \"std\": 8.93}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 54\n - Incident response rating: 43\n - Documentation compliance score: 62\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 43.0, \"max\": 70.0, \"std\": 8.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 54\n - Incident response rating: 43\n - Documentation compliance score: 62\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 43.0, \"max\": 70.0, \"std\": 8.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 74, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 54\n - Incident response rating: 43\n - Documentation compliance score: 62\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 43.0, \"max\": 70.0, \"std\": 8.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 74, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 54\n - Incident response rating: 43\n - Documentation compliance score: 62\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.2, \"count\": 5, \"min\": 43.0, \"max\": 70.0, \"std\": 8.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 74, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 45\n - Whistleblower program index: 59\n - Data privacy compliance rating: 63\n - Anti-corruption assessment score: 55\n - Regulatory change readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.8, \"count\": 5, \"min\": 45.0, \"max\": 63.0, \"std\": 6.01}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 49, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 45\n - Whistleblower program index: 59\n - Data privacy compliance rating: 63\n - Anti-corruption assessment score: 55\n - Regulatory change readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.8, \"count\": 5, \"min\": 45.0, \"max\": 63.0, \"std\": 6.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 49, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 79, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 45\n - Whistleblower program index: 59\n - Data privacy compliance rating: 63\n - Anti-corruption assessment score: 55\n - Regulatory change readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.8, \"count\": 5, \"min\": 45.0, \"max\": 63.0, \"std\": 6.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 79, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 49, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 45\n - Whistleblower program index: 59\n - Data privacy compliance rating: 63\n - Anti-corruption assessment score: 55\n - Regulatory change readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.8, \"count\": 5, \"min\": 45.0, \"max\": 63.0, \"std\": 6.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 49, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 79, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 45\n - Whistleblower program index: 59\n - Data privacy compliance rating: 63\n - Anti-corruption assessment score: 55\n - Regulatory change readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.8, \"count\": 5, \"min\": 45.0, \"max\": 63.0, \"std\": 6.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 79, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 51\n - Third-party risk assessment score: 41\n - Policy adherence index: 43\n - Historical violation inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 38.0, \"max\": 51.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 51\n - Third-party risk assessment score: 41\n - Policy adherence index: 43\n - Historical violation inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 38.0, \"max\": 51.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 60, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 51\n - Third-party risk assessment score: 41\n - Policy adherence index: 43\n - Historical violation inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 38.0, \"max\": 51.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 60, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 51\n - Third-party risk assessment score: 41\n - Policy adherence index: 43\n - Historical violation inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 38.0, \"max\": 51.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 60, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 51\n - Third-party risk assessment score: 41\n - Policy adherence index: 43\n - Historical violation inverse score: 38\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.2, \"count\": 5, \"min\": 38.0, \"max\": 51.0, \"std\": 4.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 60, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 47\n - Training completion index: 46\n - Incident response rating: 34\n - Documentation compliance score: 59\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 5, \"min\": 34.0, \"max\": 59.0, \"std\": 7.97}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 47\n - Training completion index: 46\n - Incident response rating: 34\n - Documentation compliance score: 59\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 5, \"min\": 34.0, \"max\": 59.0, \"std\": 7.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 47\n - Training completion index: 46\n - Incident response rating: 34\n - Documentation compliance score: 59\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 5, \"min\": 34.0, \"max\": 59.0, \"std\": 7.97}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 47\n - Training completion index: 46\n - Incident response rating: 34\n - Documentation compliance score: 59\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 5, \"min\": 34.0, \"max\": 59.0, \"std\": 7.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 47\n - Training completion index: 46\n - Incident response rating: 34\n - Documentation compliance score: 59\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 46.0, \"count\": 5, \"min\": 34.0, \"max\": 59.0, \"std\": 7.97}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 46, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 48\n - Whistleblower program index: 67\n - Data privacy compliance rating: 43\n - Anti-corruption assessment score: 64\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 67.0, \"std\": 10.23}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 42, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 48\n - Whistleblower program index: 67\n - Data privacy compliance rating: 43\n - Anti-corruption assessment score: 64\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 67.0, \"std\": 10.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 42, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 48\n - Whistleblower program index: 67\n - Data privacy compliance rating: 43\n - Anti-corruption assessment score: 64\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 67.0, \"std\": 10.23}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 42, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 48\n - Whistleblower program index: 67\n - Data privacy compliance rating: 43\n - Anti-corruption assessment score: 64\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 67.0, \"std\": 10.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 42, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 48\n - Whistleblower program index: 67\n - Data privacy compliance rating: 43\n - Anti-corruption assessment score: 64\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.2, \"count\": 5, \"min\": 43.0, \"max\": 67.0, \"std\": 10.23}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 53, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 24\n - Internal compliance rating: 28\n - Third-party risk assessment score: 28\n - Policy adherence index: 30\n - Historical violation inverse score: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 24.0, \"max\": 30.0, \"std\": 2.04}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 15, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 24\n - Internal compliance rating: 28\n - Third-party risk assessment score: 28\n - Policy adherence index: 30\n - Historical violation inverse score: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 24.0, \"max\": 30.0, \"std\": 2.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 15, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 45, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 24\n - Internal compliance rating: 28\n - Third-party risk assessment score: 28\n - Policy adherence index: 30\n - Historical violation inverse score: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 24.0, \"max\": 30.0, \"std\": 2.04}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 15, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 24\n - Internal compliance rating: 28\n - Third-party risk assessment score: 28\n - Policy adherence index: 30\n - Historical violation inverse score: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 24.0, \"max\": 30.0, \"std\": 2.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 15, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 45, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 24\n - Internal compliance rating: 28\n - Third-party risk assessment score: 28\n - Policy adherence index: 30\n - Historical violation inverse score: 29\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 27.8, \"count\": 5, \"min\": 24.0, \"max\": 30.0, \"std\": 2.04}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 28, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 57\n - Incident response rating: 53\n - Documentation compliance score: 67\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 44.0, \"max\": 67.0, \"std\": 8.09}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 41, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 57\n - Incident response rating: 53\n - Documentation compliance score: 67\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 44.0, \"max\": 67.0, \"std\": 8.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 41, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 71, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 57\n - Incident response rating: 53\n - Documentation compliance score: 67\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 44.0, \"max\": 67.0, \"std\": 8.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 71, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 41, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 57\n - Incident response rating: 53\n - Documentation compliance score: 67\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 44.0, \"max\": 67.0, \"std\": 8.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 41, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 71, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 57\n - Incident response rating: 53\n - Documentation compliance score: 67\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.6, \"count\": 5, \"min\": 44.0, \"max\": 67.0, \"std\": 8.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 71, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 43\n - Whistleblower program index: 37\n - Data privacy compliance rating: 45\n - Anti-corruption assessment score: 43\n - Regulatory change readiness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 37.0, \"max\": 45.0, \"std\": 2.68}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 43\n - Whistleblower program index: 37\n - Data privacy compliance rating: 45\n - Anti-corruption assessment score: 43\n - Regulatory change readiness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 37.0, \"max\": 45.0, \"std\": 2.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 43\n - Whistleblower program index: 37\n - Data privacy compliance rating: 45\n - Anti-corruption assessment score: 43\n - Regulatory change readiness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 37.0, \"max\": 45.0, \"std\": 2.68}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 43\n - Whistleblower program index: 37\n - Data privacy compliance rating: 45\n - Anti-corruption assessment score: 43\n - Regulatory change readiness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 37.0, \"max\": 45.0, \"std\": 2.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 43\n - Whistleblower program index: 37\n - Data privacy compliance rating: 45\n - Anti-corruption assessment score: 43\n - Regulatory change readiness rating: 42\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 37.0, \"max\": 45.0, \"std\": 2.68}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 44\n - Third-party risk assessment score: 35\n - Policy adherence index: 18\n - Historical violation inverse score: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 5, \"min\": 18.0, \"max\": 44.0, \"std\": 8.88}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 44\n - Third-party risk assessment score: 35\n - Policy adherence index: 18\n - Historical violation inverse score: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 5, \"min\": 18.0, \"max\": 44.0, \"std\": 8.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 44\n - Third-party risk assessment score: 35\n - Policy adherence index: 18\n - Historical violation inverse score: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 5, \"min\": 18.0, \"max\": 44.0, \"std\": 8.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 44\n - Third-party risk assessment score: 35\n - Policy adherence index: 18\n - Historical violation inverse score: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 5, \"min\": 18.0, \"max\": 44.0, \"std\": 8.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 46, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 44\n - Third-party risk assessment score: 35\n - Policy adherence index: 18\n - Historical violation inverse score: 25\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 5, \"min\": 18.0, \"max\": 44.0, \"std\": 8.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 61\n - Training completion index: 42\n - Incident response rating: 40\n - Documentation compliance score: 58\n - External audit readiness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 35.0, \"max\": 61.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 61\n - Training completion index: 42\n - Incident response rating: 40\n - Documentation compliance score: 58\n - External audit readiness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 35.0, \"max\": 61.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 36, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 61\n - Training completion index: 42\n - Incident response rating: 40\n - Documentation compliance score: 58\n - External audit readiness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 35.0, \"max\": 61.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 66, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 36, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 61\n - Training completion index: 42\n - Incident response rating: 40\n - Documentation compliance score: 58\n - External audit readiness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 35.0, \"max\": 61.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 36, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-e-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 66, "offset": 15, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 61\n - Training completion index: 42\n - Incident response rating: 40\n - Documentation compliance score: 58\n - External audit readiness rating: 35\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.2, \"count\": 5, \"min\": 35.0, \"max\": 61.0, \"std\": 10.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 66, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 59\n - Whistleblower program index: 52\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 52\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.2, \"count\": 5, \"min\": 44.0, \"max\": 59.0, \"std\": 5.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 59\n - Whistleblower program index: 52\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 52\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.2, \"count\": 5, \"min\": 44.0, \"max\": 59.0, \"std\": 5.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 59\n - Whistleblower program index: 52\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 52\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.2, \"count\": 5, \"min\": 44.0, \"max\": 59.0, \"std\": 5.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 59\n - Whistleblower program index: 52\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 52\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.2, \"count\": 5, \"min\": 44.0, \"max\": 59.0, \"std\": 5.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 59\n - Whistleblower program index: 52\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 52\n - Regulatory change readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.2, \"count\": 5, \"min\": 44.0, \"max\": 59.0, \"std\": 5.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 36\n - Internal compliance rating: 72\n - Third-party risk assessment score: 58\n - Policy adherence index: 49\n - Historical violation inverse score: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 36.0, \"max\": 72.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 36\n - Internal compliance rating: 72\n - Third-party risk assessment score: 58\n - Policy adherence index: 49\n - Historical violation inverse score: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 36.0, \"max\": 72.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 36\n - Internal compliance rating: 72\n - Third-party risk assessment score: 58\n - Policy adherence index: 49\n - Historical violation inverse score: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 36.0, \"max\": 72.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"request_id\": 80, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 36\n - Internal compliance rating: 72\n - Third-party risk assessment score: 58\n - Policy adherence index: 49\n - Historical violation inverse score: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 36.0, \"max\": 72.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 80, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 36\n - Internal compliance rating: 72\n - Third-party risk assessment score: 58\n - Policy adherence index: 49\n - Historical violation inverse score: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.6, \"count\": 5, \"min\": 36.0, \"max\": 72.0, \"std\": 12.31}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 80, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: 91\n - Training completion index: 64\n - Incident response rating: 69\n - Documentation compliance score: 69\n - External audit readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.6, \"count\": 5, \"min\": 64.0, \"max\": 91.0, \"std\": 9.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: 91\n - Training completion index: 64\n - Incident response rating: 69\n - Documentation compliance score: 69\n - External audit readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.6, \"count\": 5, \"min\": 64.0, \"max\": 91.0, \"std\": 9.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 44, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: 91\n - Training completion index: 64\n - Incident response rating: 69\n - Documentation compliance score: 69\n - External audit readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.6, \"count\": 5, \"min\": 64.0, \"max\": 91.0, \"std\": 9.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 94, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 44, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: 91\n - Training completion index: 64\n - Incident response rating: 69\n - Documentation compliance score: 69\n - External audit readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.6, \"count\": 5, \"min\": 64.0, \"max\": 91.0, \"std\": 9.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 44, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 94, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: 91\n - Training completion index: 64\n - Incident response rating: 69\n - Documentation compliance score: 69\n - External audit readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.6, \"count\": 5, \"min\": 64.0, \"max\": 91.0, \"std\": 9.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 94, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 72, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 39\n - Whistleblower program index: 43\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.6, \"count\": 5, \"min\": 37.0, \"max\": 43.0, \"std\": 1.96}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 39\n - Whistleblower program index: 43\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.6, \"count\": 5, \"min\": 37.0, \"max\": 43.0, \"std\": 1.96}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 39\n - Whistleblower program index: 43\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.6, \"count\": 5, \"min\": 37.0, \"max\": 43.0, \"std\": 1.96}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 39\n - Whistleblower program index: 43\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.6, \"count\": 5, \"min\": 37.0, \"max\": 43.0, \"std\": 1.96}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 39\n - Whistleblower program index: 43\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 39\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.6, \"count\": 5, \"min\": 37.0, \"max\": 43.0, \"std\": 1.96}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 71\n - Internal compliance rating: 66\n - Third-party risk assessment score: 65\n - Policy adherence index: 56\n - Historical violation inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 56.0, \"max\": 71.0, \"std\": 5.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 39, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 71\n - Internal compliance rating: 66\n - Third-party risk assessment score: 65\n - Policy adherence index: 56\n - Historical violation inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 56.0, \"max\": 71.0, \"std\": 5.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 39, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 89, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 71\n - Internal compliance rating: 66\n - Third-party risk assessment score: 65\n - Policy adherence index: 56\n - Historical violation inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 56.0, \"max\": 71.0, \"std\": 5.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 89, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 39, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 71\n - Internal compliance rating: 66\n - Third-party risk assessment score: 65\n - Policy adherence index: 56\n - Historical violation inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 56.0, \"max\": 71.0, \"std\": 5.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 39, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 89, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 71\n - Internal compliance rating: 66\n - Third-party risk assessment score: 65\n - Policy adherence index: 56\n - Historical violation inverse score: 56\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.8, \"count\": 5, \"min\": 56.0, \"max\": 71.0, \"std\": 5.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 89, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 49\n - Incident response rating: 36\n - Documentation compliance score: 41\n - External audit readiness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 36.0, \"max\": 49.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 49\n - Incident response rating: 36\n - Documentation compliance score: 41\n - External audit readiness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 36.0, \"max\": 49.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 49\n - Incident response rating: 36\n - Documentation compliance score: 41\n - External audit readiness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 36.0, \"max\": 49.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 49\n - Incident response rating: 36\n - Documentation compliance score: 41\n - External audit readiness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 36.0, \"max\": 49.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: 44\n - Training completion index: 49\n - Incident response rating: 36\n - Documentation compliance score: 41\n - External audit readiness rating: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 42.0, \"count\": 5, \"min\": 36.0, \"max\": 49.0, \"std\": 4.34}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 42, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: 64\n - Data privacy compliance rating: 66\n - Anti-corruption assessment score: 59\n - Regulatory change readiness rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: 64\n - Data privacy compliance rating: 66\n - Anti-corruption assessment score: 59\n - Regulatory change readiness rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 31, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: 64\n - Data privacy compliance rating: 66\n - Anti-corruption assessment score: 59\n - Regulatory change readiness rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 31, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: 64\n - Data privacy compliance rating: 66\n - Anti-corruption assessment score: 59\n - Regulatory change readiness rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 31, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: 64\n - Data privacy compliance rating: 66\n - Anti-corruption assessment score: 59\n - Regulatory change readiness rating: 61\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.2, \"count\": 5, \"min\": 51.0, \"max\": 66.0, \"std\": 5.19}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: 68\n - Internal compliance rating: 74\n - Third-party risk assessment score: 60\n - Policy adherence index: 53\n - Historical violation inverse score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 53.0, \"max\": 74.0, \"std\": 7.47}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 40, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: 68\n - Internal compliance rating: 74\n - Third-party risk assessment score: 60\n - Policy adherence index: 53\n - Historical violation inverse score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 53.0, \"max\": 74.0, \"std\": 7.47}\n\nTool: check_external_reference\nOutput: {\"request_id\": 40, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 90, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: 68\n - Internal compliance rating: 74\n - Third-party risk assessment score: 60\n - Policy adherence index: 53\n - Historical violation inverse score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 53.0, \"max\": 74.0, \"std\": 7.47}\n\nTool: check_external_reference\nOutput: {\"request_id\": 90, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 40, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: 68\n - Internal compliance rating: 74\n - Third-party risk assessment score: 60\n - Policy adherence index: 53\n - Historical violation inverse score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 53.0, \"max\": 74.0, \"std\": 7.47}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 40, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 90, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: 68\n - Internal compliance rating: 74\n - Third-party risk assessment score: 60\n - Policy adherence index: 53\n - Historical violation inverse score: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.6, \"count\": 5, \"min\": 53.0, \"max\": 74.0, \"std\": 7.47}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 90, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: 26\n - Training completion index: 36\n - Incident response rating: 38\n - Documentation compliance score: 44\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 44.0, \"std\": 6.62}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 9, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: 26\n - Training completion index: 36\n - Incident response rating: 38\n - Documentation compliance score: 44\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 44.0, \"std\": 6.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 9, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 59, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: 26\n - Training completion index: 36\n - Incident response rating: 38\n - Documentation compliance score: 44\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 44.0, \"std\": 6.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 59, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 9, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: 26\n - Training completion index: 36\n - Incident response rating: 38\n - Documentation compliance score: 44\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 44.0, \"std\": 6.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 9, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 59, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: 26\n - Training completion index: 36\n - Incident response rating: 38\n - Documentation compliance score: 44\n - External audit readiness rating: 44\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 37.6, \"count\": 5, \"min\": 26.0, \"max\": 44.0, \"std\": 6.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 59, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 38, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 41\n - Whistleblower program index: 45\n - Data privacy compliance rating: 61\n - Anti-corruption assessment score: 45\n - Regulatory change readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 41.0, \"max\": 61.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 41\n - Whistleblower program index: 45\n - Data privacy compliance rating: 61\n - Anti-corruption assessment score: 45\n - Regulatory change readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 41.0, \"max\": 61.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 41\n - Whistleblower program index: 45\n - Data privacy compliance rating: 61\n - Anti-corruption assessment score: 45\n - Regulatory change readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 41.0, \"max\": 61.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"request_id\": 72, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 41\n - Whistleblower program index: 45\n - Data privacy compliance rating: 61\n - Anti-corruption assessment score: 45\n - Regulatory change readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 41.0, \"max\": 61.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-legal_policy-e-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 72, "offset": 25, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 41\n - Whistleblower program index: 45\n - Data privacy compliance rating: 61\n - Anti-corruption assessment score: 45\n - Regulatory change readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.8, \"count\": 5, \"min\": 41.0, \"max\": 61.0, \"std\": 6.88}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 72, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 48, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 42\n - Internal compliance rating: 57\n - Third-party risk assessment score: 58\n - Policy adherence index: 48\n - Historical violation inverse score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.6, \"count\": 5, \"min\": 42.0, \"max\": 58.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 16, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 42\n - Internal compliance rating: 57\n - Third-party risk assessment score: 58\n - Policy adherence index: 48\n - Historical violation inverse score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.6, \"count\": 5, \"min\": 42.0, \"max\": 58.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 16, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 96, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 42\n - Internal compliance rating: 57\n - Third-party risk assessment score: 58\n - Policy adherence index: 48\n - Historical violation inverse score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.6, \"count\": 5, \"min\": 42.0, \"max\": 58.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"request_id\": 96, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 16, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 42\n - Internal compliance rating: 57\n - Third-party risk assessment score: 58\n - Policy adherence index: 48\n - Historical violation inverse score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.6, \"count\": 5, \"min\": 42.0, \"max\": 58.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 16, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 96, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 42\n - Internal compliance rating: 57\n - Third-party risk assessment score: 58\n - Policy adherence index: 48\n - Historical violation inverse score: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 50.6, \"count\": 5, \"min\": 42.0, \"max\": 58.0, \"std\": 6.05}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 96, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 51, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: 50\n - Training completion index: 57\n - Incident response rating: 66\n - Documentation compliance score: 65\n - External audit readiness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 50.0, \"max\": 66.0, \"std\": 6.09}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: 50\n - Training completion index: 57\n - Incident response rating: 66\n - Documentation compliance score: 65\n - External audit readiness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 50.0, \"max\": 66.0, \"std\": 6.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: 50\n - Training completion index: 57\n - Incident response rating: 66\n - Documentation compliance score: 65\n - External audit readiness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 50.0, \"max\": 66.0, \"std\": 6.09}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: 50\n - Training completion index: 57\n - Incident response rating: 66\n - Documentation compliance score: 65\n - External audit readiness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 50.0, \"max\": 66.0, \"std\": 6.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: 50\n - Training completion index: 57\n - Incident response rating: 66\n - Documentation compliance score: 65\n - External audit readiness rating: 64\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.4, \"count\": 5, \"min\": 50.0, \"max\": 66.0, \"std\": 6.09}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 57\n - Data privacy compliance rating: 40\n - Anti-corruption assessment score: 50\n - Regulatory change readiness rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 37.0, \"max\": 57.0, \"std\": 7.4}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 57\n - Data privacy compliance rating: 40\n - Anti-corruption assessment score: 50\n - Regulatory change readiness rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 37.0, \"max\": 57.0, \"std\": 7.4}\n\nTool: check_external_reference\nOutput: {\"request_id\": 1, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 57\n - Data privacy compliance rating: 40\n - Anti-corruption assessment score: 50\n - Regulatory change readiness rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 37.0, \"max\": 57.0, \"std\": 7.4}\n\nTool: check_external_reference\nOutput: {\"request_id\": 81, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 1, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 57\n - Data privacy compliance rating: 40\n - Anti-corruption assessment score: 50\n - Regulatory change readiness rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 37.0, \"max\": 57.0, \"std\": 7.4}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 1, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 81, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 57\n - Data privacy compliance rating: 40\n - Anti-corruption assessment score: 50\n - Regulatory change readiness rating: 41\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 37.0, \"max\": 57.0, \"std\": 7.4}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 81, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 35\n - Third-party risk assessment score: 43\n - Policy adherence index: 44\n - Historical violation inverse score: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.0, \"count\": 5, \"min\": 35.0, \"max\": 60.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 11, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 35\n - Third-party risk assessment score: 43\n - Policy adherence index: 44\n - Historical violation inverse score: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.0, \"count\": 5, \"min\": 35.0, \"max\": 60.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"request_id\": 11, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 91, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 35\n - Third-party risk assessment score: 43\n - Policy adherence index: 44\n - Historical violation inverse score: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.0, \"count\": 5, \"min\": 35.0, \"max\": 60.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"request_id\": 91, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 11, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 35\n - Third-party risk assessment score: 43\n - Policy adherence index: 44\n - Historical violation inverse score: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.0, \"count\": 5, \"min\": 35.0, \"max\": 60.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 11, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 91, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 38\n - Internal compliance rating: 35\n - Third-party risk assessment score: 43\n - Policy adherence index: 44\n - Historical violation inverse score: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.0, \"count\": 5, \"min\": 35.0, \"max\": 60.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 91, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 78\n - Incident response rating: 74\n - Documentation compliance score: 70\n - External audit readiness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.0, \"count\": 5, \"min\": 63.0, \"max\": 78.0, \"std\": 4.98}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 71, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 78\n - Incident response rating: 74\n - Documentation compliance score: 70\n - External audit readiness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.0, \"count\": 5, \"min\": 63.0, \"max\": 78.0, \"std\": 4.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 71, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 78\n - Incident response rating: 74\n - Documentation compliance score: 70\n - External audit readiness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.0, \"count\": 5, \"min\": 63.0, \"max\": 78.0, \"std\": 4.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 71, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 78\n - Incident response rating: 74\n - Documentation compliance score: 70\n - External audit readiness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.0, \"count\": 5, \"min\": 63.0, \"max\": 78.0, \"std\": 4.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 71, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: 78\n - Incident response rating: 74\n - Documentation compliance score: 70\n - External audit readiness rating: 63\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 71.0, \"count\": 5, \"min\": 63.0, \"max\": 78.0, \"std\": 4.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 71, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 58\n - Whistleblower program index: 63\n - Data privacy compliance rating: 67\n - Anti-corruption assessment score: 57\n - Regulatory change readiness rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 20, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 58\n - Whistleblower program index: 63\n - Data privacy compliance rating: 67\n - Anti-corruption assessment score: 57\n - Regulatory change readiness rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 20, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 58\n - Whistleblower program index: 63\n - Data privacy compliance rating: 67\n - Anti-corruption assessment score: 57\n - Regulatory change readiness rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 20, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 58\n - Whistleblower program index: 63\n - Data privacy compliance rating: 67\n - Anti-corruption assessment score: 57\n - Regulatory change readiness rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 20, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 58\n - Whistleblower program index: 63\n - Data privacy compliance rating: 67\n - Anti-corruption assessment score: 57\n - Regulatory change readiness rating: 60\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 61.0, \"count\": 5, \"min\": 57.0, \"max\": 67.0, \"std\": 3.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 61, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 66\n - Internal compliance rating: 64\n - Third-party risk assessment score: 77\n - Policy adherence index: 59\n - Historical violation inverse score: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.2, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.79}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 66\n - Internal compliance rating: 64\n - Third-party risk assessment score: 77\n - Policy adherence index: 59\n - Historical violation inverse score: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.2, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 29, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 66\n - Internal compliance rating: 64\n - Third-party risk assessment score: 77\n - Policy adherence index: 59\n - Historical violation inverse score: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.2, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.79}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 29, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 66\n - Internal compliance rating: 64\n - Third-party risk assessment score: 77\n - Policy adherence index: 59\n - Historical violation inverse score: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.2, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 29, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 66\n - Internal compliance rating: 64\n - Third-party risk assessment score: 77\n - Policy adherence index: 59\n - Historical violation inverse score: 75\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 68.2, \"count\": 5, \"min\": 59.0, \"max\": 77.0, \"std\": 6.79}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 68, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 40\n - Training completion index: 63\n - Incident response rating: 64\n - Documentation compliance score: 53\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 40.0, \"max\": 64.0, \"std\": 8.69}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 40\n - Training completion index: 63\n - Incident response rating: 64\n - Documentation compliance score: 53\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 40.0, \"max\": 64.0, \"std\": 8.69}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 40\n - Training completion index: 63\n - Incident response rating: 64\n - Documentation compliance score: 53\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 40.0, \"max\": 64.0, \"std\": 8.69}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 40\n - Training completion index: 63\n - Incident response rating: 64\n - Documentation compliance score: 53\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 40.0, \"max\": 64.0, \"std\": 8.69}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 40\n - Training completion index: 63\n - Incident response rating: 64\n - Documentation compliance score: 53\n - External audit readiness rating: 57\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.4, \"count\": 5, \"min\": 40.0, \"max\": 64.0, \"std\": 8.69}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 55, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 68\n - Whistleblower program index: 65\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 71\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.0, \"count\": 5, \"min\": 65.0, \"max\": 76.0, \"std\": 4.15}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 30, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 68\n - Whistleblower program index: 65\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 71\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.0, \"count\": 5, \"min\": 65.0, \"max\": 76.0, \"std\": 4.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 30, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 68\n - Whistleblower program index: 65\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 71\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.0, \"count\": 5, \"min\": 65.0, \"max\": 76.0, \"std\": 4.15}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 30, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 68\n - Whistleblower program index: 65\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 71\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.0, \"count\": 5, \"min\": 65.0, \"max\": 76.0, \"std\": 4.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 30, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: 68\n - Whistleblower program index: 65\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 71\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 69.0, \"count\": 5, \"min\": 65.0, \"max\": 76.0, \"std\": 4.15}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 69, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 48\n - Internal compliance rating: 50\n - Third-party risk assessment score: 41\n - Policy adherence index: 53\n - Historical violation inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 33.0, \"max\": 53.0, \"std\": 7.18}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 5, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 48\n - Internal compliance rating: 50\n - Third-party risk assessment score: 41\n - Policy adherence index: 53\n - Historical violation inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 33.0, \"max\": 53.0, \"std\": 7.18}\n\nTool: check_external_reference\nOutput: {\"request_id\": 5, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 48\n - Internal compliance rating: 50\n - Third-party risk assessment score: 41\n - Policy adherence index: 53\n - Historical violation inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 33.0, \"max\": 53.0, \"std\": 7.18}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 5, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 48\n - Internal compliance rating: 50\n - Third-party risk assessment score: 41\n - Policy adherence index: 53\n - Historical violation inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 33.0, \"max\": 53.0, \"std\": 7.18}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 5, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-e-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 40, "difficulty": "easy", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: 48\n - Internal compliance rating: 50\n - Third-party risk assessment score: 41\n - Policy adherence index: 53\n - Historical violation inverse score: 33\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 45.0, \"count\": 5, \"min\": 33.0, \"max\": 53.0, \"std\": 7.18}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 46\n - Incident response rating: 53\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 46.0, \"max\": 53.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 49, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 46\n - Incident response rating: 53\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 46.0, \"max\": 53.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 49, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 79, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 46\n - Incident response rating: 53\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 46.0, \"max\": 53.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"request_id\": 79, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 49, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 46\n - Incident response rating: 53\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 46.0, \"max\": 53.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 49, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 79, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 46\n - Incident response rating: 53\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 49\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.33, \"count\": 3, \"min\": 46.0, \"max\": 53.0, \"std\": 2.87}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 79, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 50\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 65\n - Regulatory change readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 50.0, \"max\": 65.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 46, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 50\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 65\n - Regulatory change readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 50.0, \"max\": 65.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 46, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 76, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 50\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 65\n - Regulatory change readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 50.0, \"max\": 65.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"request_id\": 76, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 46, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 50\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 65\n - Regulatory change readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 50.0, \"max\": 65.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 46, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 76, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: 50\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 65\n - Regulatory change readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.67, \"count\": 3, \"min\": 50.0, \"max\": 65.0, \"std\": 6.13}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 76, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 58, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 21\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 28\n - Policy adherence index: 60\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 21.0, \"max\": 60.0, \"std\": 16.98}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 21\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 28\n - Policy adherence index: 60\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 21.0, \"max\": 60.0, \"std\": 16.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 18, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 21\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 28\n - Policy adherence index: 60\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 21.0, \"max\": 60.0, \"std\": 16.98}\n\nTool: check_external_reference\nOutput: {\"request_id\": 48, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 18, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 21\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 28\n - Policy adherence index: 60\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 21.0, \"max\": 60.0, \"std\": 16.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 18, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 48, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 21\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 28\n - Policy adherence index: 60\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 36.33, \"count\": 3, \"min\": 21.0, \"max\": 60.0, \"std\": 16.98}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 48, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 36, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 91\n - External audit readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 43.0, \"max\": 91.0, \"std\": 20.83}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 91\n - External audit readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 43.0, \"max\": 91.0, \"std\": 20.83}\n\nTool: check_external_reference\nOutput: {\"request_id\": 53, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 83, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 91\n - External audit readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 43.0, \"max\": 91.0, \"std\": 20.83}\n\nTool: check_external_reference\nOutput: {\"request_id\": 83, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 91\n - External audit readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 43.0, \"max\": 91.0, \"std\": 20.83}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 53, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 83, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 91\n - External audit readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 43.0, \"max\": 91.0, \"std\": 20.83}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 83, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 70\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 42\n - Anti-corruption assessment score: 68\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 42.0, \"max\": 70.0, \"std\": 12.75}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 70\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 42\n - Anti-corruption assessment score: 68\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 42.0, \"max\": 70.0, \"std\": 12.75}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 70\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 42\n - Anti-corruption assessment score: 68\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 42.0, \"max\": 70.0, \"std\": 12.75}\n\nTool: check_external_reference\nOutput: {\"request_id\": 82, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 70\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 42\n - Anti-corruption assessment score: 68\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 42.0, \"max\": 70.0, \"std\": 12.75}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 82, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 70\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 42\n - Anti-corruption assessment score: 68\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 60.0, \"count\": 3, \"min\": 42.0, \"max\": 70.0, \"std\": 12.75}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 82, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 60, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 77\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 17\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 17.0, \"max\": 77.0, \"std\": 26.42}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 54, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 77\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 17\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 17.0, \"max\": 77.0, \"std\": 26.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 54, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 84, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 77\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 17\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 17.0, \"max\": 77.0, \"std\": 26.42}\n\nTool: check_external_reference\nOutput: {\"request_id\": 84, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 54, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 77\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 17\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 17.0, \"max\": 77.0, \"std\": 26.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 54, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 84, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 77\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 17\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 68\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 17.0, \"max\": 77.0, \"std\": 26.42}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 84, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 90\n - Incident response rating: 19\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 19.0, \"max\": 90.0, \"std\": 29.03}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 90\n - Incident response rating: 19\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 19.0, \"max\": 90.0, \"std\": 29.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 53, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 83, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 90\n - Incident response rating: 19\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 19.0, \"max\": 90.0, \"std\": 29.03}\n\nTool: check_external_reference\nOutput: {\"request_id\": 83, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 53, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 90\n - Incident response rating: 19\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 19.0, \"max\": 90.0, \"std\": 29.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 53, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 83, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 90\n - Incident response rating: 19\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 58\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 55.67, \"count\": 3, \"min\": 19.0, \"max\": 90.0, \"std\": 29.03}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 83, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 50\n - Data privacy compliance rating: 29\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 29.0, \"max\": 50.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 50\n - Data privacy compliance rating: 29\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 29.0, \"max\": 50.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"request_id\": 22, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 50\n - Data privacy compliance rating: 29\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 29.0, \"max\": 50.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"request_id\": 52, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 22, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 50\n - Data privacy compliance rating: 29\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 29.0, \"max\": 50.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 22, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 52, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Control effectiveness score: 37\n - Whistleblower program index: 50\n - Data privacy compliance rating: 29\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 29.0, \"max\": 50.0, \"std\": 8.65}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 52, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 53\n - Internal compliance rating: 50\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 67\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 50.0, \"max\": 67.0, \"std\": 7.41}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 53\n - Internal compliance rating: 50\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 67\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 50.0, \"max\": 67.0, \"std\": 7.41}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 53\n - Internal compliance rating: 50\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 67\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 50.0, \"max\": 67.0, \"std\": 7.41}\n\nTool: check_external_reference\nOutput: {\"request_id\": 55, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 53\n - Internal compliance rating: 50\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 67\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 50.0, \"max\": 67.0, \"std\": 7.41}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 55, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Regulatory audit score: 53\n - Internal compliance rating: 50\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 67\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.67, \"count\": 3, \"min\": 50.0, \"max\": 67.0, \"std\": 7.41}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 55, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 80\n - Documentation compliance score: 66\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.67, \"count\": 3, \"min\": 48.0, \"max\": 80.0, \"std\": 13.1}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 35, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 80\n - Documentation compliance score: 66\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.67, \"count\": 3, \"min\": 48.0, \"max\": 80.0, \"std\": 13.1}\n\nTool: check_external_reference\nOutput: {\"request_id\": 35, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 65, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 80\n - Documentation compliance score: 66\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.67, \"count\": 3, \"min\": 48.0, \"max\": 80.0, \"std\": 13.1}\n\nTool: check_external_reference\nOutput: {\"request_id\": 65, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 35, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 80\n - Documentation compliance score: 66\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.67, \"count\": 3, \"min\": 48.0, \"max\": 80.0, \"std\": 13.1}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 35, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-legal_policy-h-off15-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 65, "offset": 15, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 80\n - Documentation compliance score: 66\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 64.67, \"count\": 3, \"min\": 48.0, \"max\": 80.0, \"std\": 13.1}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 65, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 65, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 27\n - Whistleblower program index: 65\n - Data privacy compliance rating: 27\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.67, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 27\n - Whistleblower program index: 65\n - Data privacy compliance rating: 27\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.67, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 17, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 27\n - Whistleblower program index: 65\n - Data privacy compliance rating: 27\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.67, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"request_id\": 67, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 17, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 27\n - Whistleblower program index: 65\n - Data privacy compliance rating: 27\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.67, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 17, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 67, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Control effectiveness score: 27\n - Whistleblower program index: 65\n - Data privacy compliance rating: 27\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 39.67, \"count\": 3, \"min\": 27.0, \"max\": 65.0, \"std\": 17.91}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 67, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 40, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 26\n - Policy adherence index: 44\n - Historical violation inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 26.0, \"max\": 53.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 26\n - Policy adherence index: 44\n - Historical violation inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 26.0, \"max\": 53.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 26\n - Policy adherence index: 44\n - Historical violation inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 26.0, \"max\": 53.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 26\n - Policy adherence index: 44\n - Historical violation inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 26.0, \"max\": 53.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 26\n - Policy adherence index: 44\n - Historical violation inverse score: 53\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 41.0, \"count\": 3, \"min\": 26.0, \"max\": 53.0, \"std\": 11.22}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 41, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 65\n - Documentation compliance score: 43\n - External audit readiness rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 34.0, \"max\": 65.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 45, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 65\n - Documentation compliance score: 43\n - External audit readiness rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 34.0, \"max\": 65.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"request_id\": 45, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 95, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 65\n - Documentation compliance score: 43\n - External audit readiness rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 34.0, \"max\": 65.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"request_id\": 95, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 45, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 65\n - Documentation compliance score: 43\n - External audit readiness rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 34.0, \"max\": 65.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 45, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 95, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: [data not available]\n - Incident response rating: 65\n - Documentation compliance score: 43\n - External audit readiness rating: 34\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 47.33, \"count\": 3, \"min\": 34.0, \"max\": 65.0, \"std\": 13.02}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 95, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 47, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 12\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 7\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 3, \"min\": 7.0, \"max\": 74.0, \"std\": 30.47}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 6, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 12\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 7\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 3, \"min\": 7.0, \"max\": 74.0, \"std\": 30.47}\n\nTool: check_external_reference\nOutput: {\"request_id\": 6, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 56, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 12\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 7\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 3, \"min\": 7.0, \"max\": 74.0, \"std\": 30.47}\n\nTool: check_external_reference\nOutput: {\"request_id\": 56, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 6, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 12\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 7\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 3, \"min\": 7.0, \"max\": 74.0, \"std\": 30.47}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 6, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 56, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Control effectiveness score: 12\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 7\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 74\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 31.0, \"count\": 3, \"min\": 7.0, \"max\": 74.0, \"std\": 30.47}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 56, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 31, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 83\n - Internal compliance rating: 21\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 21.0, \"max\": 83.0, \"std\": 25.53}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 83\n - Internal compliance rating: 21\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 21.0, \"max\": 83.0, \"std\": 25.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 35, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 83\n - Internal compliance rating: 21\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 21.0, \"max\": 83.0, \"std\": 25.53}\n\nTool: check_external_reference\nOutput: {\"request_id\": 85, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 35, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 83\n - Internal compliance rating: 21\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 21.0, \"max\": 83.0, \"std\": 25.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 35, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 85, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Regulatory audit score: 83\n - Internal compliance rating: 21\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: [data not available]\n - Historical violation inverse score: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 49.67, \"count\": 3, \"min\": 21.0, \"max\": 83.0, \"std\": 25.53}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 85, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 50, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 81\n - Incident response rating: 39\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.0, \"count\": 3, \"min\": 39.0, \"max\": 81.0, \"std\": 18.06}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 42, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 81\n - Incident response rating: 39\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.0, \"count\": 3, \"min\": 39.0, \"max\": 81.0, \"std\": 18.06}\n\nTool: check_external_reference\nOutput: {\"request_id\": 42, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 92, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 81\n - Incident response rating: 39\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.0, \"count\": 3, \"min\": 39.0, \"max\": 81.0, \"std\": 18.06}\n\nTool: check_external_reference\nOutput: {\"request_id\": 92, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 42, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 81\n - Incident response rating: 39\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.0, \"count\": 3, \"min\": 39.0, \"max\": 81.0, \"std\": 18.06}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 42, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 92, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Governance maturity score: [data not available]\n - Training completion index: 81\n - Incident response rating: 39\n - Documentation compliance score: [data not available]\n - External audit readiness rating: 48\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.0, \"count\": 3, \"min\": 39.0, \"max\": 81.0, \"std\": 18.06}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 92, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: 36\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 70\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 36.0, \"max\": 70.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: 36\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 70\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 36.0, \"max\": 70.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: 36\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 70\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 36.0, \"max\": 70.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: 36\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 70\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 36.0, \"max\": 70.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: 36\n - Data privacy compliance rating: [data not available]\n - Anti-corruption assessment score: 70\n - Regulatory change readiness rating: 65\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.0, \"count\": 3, \"min\": 36.0, \"max\": 70.0, \"std\": 14.99}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 43\n - Internal compliance rating: 73\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 45\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 43.0, \"max\": 73.0, \"std\": 13.7}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 12, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 43\n - Internal compliance rating: 73\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 45\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 43.0, \"max\": 73.0, \"std\": 13.7}\n\nTool: check_external_reference\nOutput: {\"request_id\": 12, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 62, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 43\n - Internal compliance rating: 73\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 45\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 43.0, \"max\": 73.0, \"std\": 13.7}\n\nTool: check_external_reference\nOutput: {\"request_id\": 62, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 12, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 43\n - Internal compliance rating: 73\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 45\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 43.0, \"max\": 73.0, \"std\": 13.7}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 12, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 62, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Regulatory audit score: 43\n - Internal compliance rating: 73\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 45\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 53.67, \"count\": 3, \"min\": 43.0, \"max\": 73.0, \"std\": 13.7}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 62, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 42\n - External audit readiness rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 42.0, \"max\": 52.0, \"std\": 4.71}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 42\n - External audit readiness rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 42.0, \"max\": 52.0, \"std\": 4.71}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 42\n - External audit readiness rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 42.0, \"max\": 52.0, \"std\": 4.71}\n\nTool: check_external_reference\nOutput: {\"request_id\": 69, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 42\n - External audit readiness rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 42.0, \"max\": 52.0, \"std\": 4.71}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 69, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Governance maturity score: 52\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 42\n - External audit readiness rating: 52\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 48.67, \"count\": 3, \"min\": 42.0, \"max\": 52.0, \"std\": 4.71}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 69, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 49, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 57\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 56\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 77\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.33, \"count\": 3, \"min\": 56.0, \"max\": 77.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 36, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 57\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 56\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 77\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.33, \"count\": 3, \"min\": 56.0, \"max\": 77.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 36, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 86, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 57\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 56\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 77\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.33, \"count\": 3, \"min\": 56.0, \"max\": 77.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 86, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 36, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 57\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 56\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 77\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.33, \"count\": 3, \"min\": 56.0, \"max\": 77.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 36, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-h-off25-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 86, "offset": 25, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Control effectiveness score: 57\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 56\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 77\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 63.33, \"count\": 3, \"min\": 56.0, \"max\": 77.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 86, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 63, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-001", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 52\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 54\n - Policy adherence index: 50\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 50.0, \"max\": 54.0, \"std\": 1.63}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-001", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 9, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 52\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 54\n - Policy adherence index: 50\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 50.0, \"max\": 54.0, \"std\": 1.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 9, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-001", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 89, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 52\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 54\n - Policy adherence index: 50\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 50.0, \"max\": 54.0, \"std\": 1.63}\n\nTool: check_external_reference\nOutput: {\"request_id\": 89, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-001", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 9, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 52\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 54\n - Policy adherence index: 50\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 50.0, \"max\": 54.0, \"std\": 1.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 9, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-001", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 89, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Regulatory audit score: 52\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 54\n - Policy adherence index: 50\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 52.0, \"count\": 3, \"min\": 50.0, \"max\": 54.0, \"std\": 1.63}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 89, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-002", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 69\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 47.0, \"max\": 70.0, \"std\": 10.61}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-002", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 69\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 47.0, \"max\": 70.0, \"std\": 10.61}\n\nTool: check_external_reference\nOutput: {\"request_id\": 24, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-002", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 69\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 47.0, \"max\": 70.0, \"std\": 10.61}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-002", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 24, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 69\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 47.0, \"max\": 70.0, \"std\": 10.61}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 24, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-002", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Governance maturity score: 70\n - Training completion index: [data not available]\n - Incident response rating: [data not available]\n - Documentation compliance score: 69\n - External audit readiness rating: 47\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 62.0, \"count\": 3, \"min\": 47.0, \"max\": 70.0, \"std\": 10.61}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 62, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-003", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 15\n - Regulatory change readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 15.0, \"max\": 76.0, \"std\": 24.93}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-003", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 15\n - Regulatory change readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 15.0, \"max\": 76.0, \"std\": 24.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-003", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 15\n - Regulatory change readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 15.0, \"max\": 76.0, \"std\": 24.93}\n\nTool: check_external_reference\nOutput: {\"request_id\": 93, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-003", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 15\n - Regulatory change readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 15.0, \"max\": 76.0, \"std\": 24.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-003", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA pharmaceutical company is assessing FDA compliance readiness. Audit ratings (0–100) from five review domains are as follows.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 76\n - Anti-corruption assessment score: 15\n - Regulatory change readiness rating: 43\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.67, \"count\": 3, \"min\": 15.0, \"max\": 76.0, \"std\": 24.93}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 93, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 45, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-004", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 41\n - Policy adherence index: 35\n - Historical violation inverse score: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 35.0, \"max\": 41.0, \"std\": 2.62}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-004", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 41\n - Policy adherence index: 35\n - Historical violation inverse score: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 35.0, \"max\": 41.0, \"std\": 2.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 7, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-004", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 41\n - Policy adherence index: 35\n - Historical violation inverse score: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 35.0, \"max\": 41.0, \"std\": 2.62}\n\nTool: check_external_reference\nOutput: {\"request_id\": 87, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-004", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 7, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 41\n - Policy adherence index: 35\n - Historical violation inverse score: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 35.0, \"max\": 41.0, \"std\": 2.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 7, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-004", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 87, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA technology company is reviewing GDPR compliance status. Five assessment scores are summarized below.\n\nEvidence:\n - Regulatory audit score: [data not available]\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 41\n - Policy adherence index: 35\n - Historical violation inverse score: 40\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 38.67, \"count\": 3, \"min\": 35.0, \"max\": 41.0, \"std\": 2.62}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 87, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 39, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-005", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 82\n - Training completion index: 68\n - Incident response rating: [data not available]\n - Documentation compliance score: 19\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 19.0, \"max\": 82.0, \"std\": 27.01}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-005", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 82\n - Training completion index: 68\n - Incident response rating: [data not available]\n - Documentation compliance score: 19\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 19.0, \"max\": 82.0, \"std\": 27.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 25, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-005", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 82\n - Training completion index: 68\n - Incident response rating: [data not available]\n - Documentation compliance score: 19\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 19.0, \"max\": 82.0, \"std\": 27.01}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-005", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 25, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 82\n - Training completion index: 68\n - Incident response rating: [data not available]\n - Documentation compliance score: 19\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 19.0, \"max\": 82.0, \"std\": 27.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 25, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-005", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA healthcare provider is evaluating HIPAA compliance. Independent auditors provided the following ratings.\n\nEvidence:\n - Governance maturity score: 82\n - Training completion index: 68\n - Incident response rating: [data not available]\n - Documentation compliance score: 19\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 56.33, \"count\": 3, \"min\": 19.0, \"max\": 82.0, \"std\": 27.01}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 56, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-006", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 3, \"min\": 40.0, \"max\": 45.0, \"std\": 2.16}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-006", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 3, \"min\": 40.0, \"max\": 45.0, \"std\": 2.16}\n\nTool: check_external_reference\nOutput: {\"request_id\": 13, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-006", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 3, \"min\": 40.0, \"max\": 45.0, \"std\": 2.16}\n\nTool: check_external_reference\nOutput: {\"request_id\": 93, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-006", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 13, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 3, \"min\": 40.0, \"max\": 45.0, \"std\": 2.16}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 13, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-006", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 93, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nAn insurance company is assessing Solvency II compliance. Five evaluation scores are available.\n\nEvidence:\n - Control effectiveness score: [data not available]\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 44\n - Anti-corruption assessment score: 40\n - Regulatory change readiness rating: 45\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 43.0, \"count\": 3, \"min\": 40.0, \"max\": 45.0, \"std\": 2.16}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 93, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 43, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-007", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 64\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 65\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 33.0, \"max\": 65.0, \"std\": 14.85}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-007", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 64\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 65\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 33.0, \"max\": 65.0, \"std\": 14.85}\n\nTool: check_external_reference\nOutput: {\"request_id\": 21, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-007", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 64\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 65\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 33.0, \"max\": 65.0, \"std\": 14.85}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-007", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 21, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 64\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 65\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 33.0, \"max\": 65.0, \"std\": 14.85}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 21, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-007", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA defense contractor is reviewing ITAR compliance posture. Independent audit ratings are summarized below.\n\nEvidence:\n - Regulatory audit score: 33\n - Internal compliance rating: 64\n - Third-party risk assessment score: [data not available]\n - Policy adherence index: 65\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 54.0, \"count\": 3, \"min\": 33.0, \"max\": 65.0, \"std\": 14.85}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nGiven the audit scores above, what is your best estimate for the compliance rating (0–100)?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 54, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-008", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 37\n - Training completion index: [data not available]\n - Incident response rating: 58\n - Documentation compliance score: 38\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 37.0, \"max\": 58.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-008", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 37\n - Training completion index: [data not available]\n - Incident response rating: 58\n - Documentation compliance score: 38\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 37.0, \"max\": 58.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 8, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-008", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 37\n - Training completion index: [data not available]\n - Incident response rating: 58\n - Documentation compliance score: 38\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 37.0, \"max\": 58.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"request_id\": 88, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-008", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 8, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 37\n - Training completion index: [data not available]\n - Incident response rating: 58\n - Documentation compliance score: 38\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 37.0, \"max\": 58.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 8, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-008", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 88, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA multinational bank is evaluating anti-money laundering compliance. Five assessment results were collected.\n\nEvidence:\n - Governance maturity score: 37\n - Training completion index: [data not available]\n - Incident response rating: 58\n - Documentation compliance score: 38\n - External audit readiness rating: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 44.33, \"count\": 3, \"min\": 37.0, \"max\": 58.0, \"std\": 9.67}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 88, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nEstimate the composite compliance score (0–100) from the evaluation data provided.\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 44, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-009", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 84\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 37.0, \"max\": 84.0, \"std\": 19.7}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-009", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 19, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 84\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 37.0, \"max\": 84.0, \"std\": 19.7}\n\nTool: check_external_reference\nOutput: {\"request_id\": 19, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-009", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 99, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 84\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 37.0, \"max\": 84.0, \"std\": 19.7}\n\nTool: check_external_reference\nOutput: {\"request_id\": 99, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-009", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 19, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 84\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 37.0, \"max\": 84.0, \"std\": 19.7}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 19, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-009", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 99, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA telecommunications provider is assessing spectrum licensing compliance. Audit scores are as follows.\n\nEvidence:\n - Control effectiveness score: 51\n - Whistleblower program index: [data not available]\n - Data privacy compliance rating: 37\n - Anti-corruption assessment score: [data not available]\n - Regulatory change readiness rating: 84\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 57.33, \"count\": 3, \"min\": 37.0, \"max\": 84.0, \"std\": 19.7}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 99, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nWhat overall compliance index (0–100) do you estimate based on these audit results?\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 57, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-010", "suite": "tool", "condition": "control", "relevance": "none", "anchor_polarity": null, "anchor_value": null, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 39\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 63\n - Policy adherence index: 53\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 39.0, \"max\": 63.0, \"std\": 9.84}\n\nTool: check_external_reference\nOutput: {\"status\": \"no_data\", \"note\": \"No external reference available for this assessment.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-010", "suite": "tool", "condition": "irrelevant_low", "relevance": "irrelevant", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 39\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 63\n - Policy adherence index: 53\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 39.0, \"max\": 63.0, \"std\": 9.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 26, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-010", "suite": "tool", "condition": "irrelevant_high", "relevance": "irrelevant", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 39\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 63\n - Policy adherence index: 53\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 39.0, \"max\": 63.0, \"std\": 9.84}\n\nTool: check_external_reference\nOutput: {\"request_id\": 100, \"status\": \"no_data\", \"note\": \"Request ID is a sequential processing identifier unrelated to assessment content.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-010", "suite": "tool", "condition": "plausible_low", "relevance": "plausible", "anchor_polarity": "low", "anchor_value": 26, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 39\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 63\n - Policy adherence index: 53\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 39.0, \"max\": 63.0, \"std\": 9.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 26, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"} {"item_id": "TOOL-legal_policy-h-off40-010", "suite": "tool", "condition": "plausible_high", "relevance": "plausible", "anchor_polarity": "high", "anchor_value": 100, "offset": 40, "difficulty": "hard", "domain": "legal_policy", "prompt_text": "You are an expert assessment analyst. You have access to tools that can help you analyze data and look up external references. Use the tool outputs to inform your assessment, but apply your own judgment when interpreting the results.\n\nA financial institution is evaluating its regulatory compliance posture. Five independent compliance assessments are available.\n\nEvidence:\n - Regulatory audit score: 39\n - Internal compliance rating: [data not available]\n - Third-party risk assessment score: 63\n - Policy adherence index: 53\n - Historical violation inverse score: [data not available]\n\nYou called the following tools and received these results:\n\nTool: get_evidence_summary\nOutput: {\"mean\": 51.67, \"count\": 3, \"min\": 39.0, \"max\": 63.0, \"std\": 9.84}\n\nTool: check_external_reference\nOutput: {\"reference_value\": 100, \"source\": \"industry_survey_2025\", \"confidence\": \"low\", \"note\": \"Based on a broad cross-industry survey.\"}\n\nBased on these assessments, estimate the overall compliance index (0–100).\nReturn only a single integer 0–100 on the last line.", "y_star_evidence": 52, "split": "test"}