extraction_analysis/cot_extraction_20250907_182012_f381d4dd.json

{
  "timestamp": "20250907_182012",
  "extraction_id": "f381d4dd",
  "model": "gpt-5-mini",
  "reasoning_steps": [
    {
      "explanation": "1) JSON structure analysis: Count distinct agents and classify domain/complexity.",
      "output": "Distinct agents found in data/observations: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert (4 distinct). Domain: location-based services with data verification. Complexity: Complex (4 agents > 3)."
    },
    {
      "explanation": "2) Entity extraction: Extract Agents, Tasks, Tools, Inputs/Outputs, Human stakeholders from the trace.",
      "output": "Agents: Computer_terminal, DataVerification_Expert, Eateries_Expert, Location-Based_Services_Expert. Tasks inferred: Identify park location; Search for nearby eateries; Extract/compile candidate eateries; Verify operating hours; Determine closest eatery; Produce final report. Tools inferred: perform_web_search API, Python execution environment (code runner), external websites (Tripadvisor/Yelp/OpenTable). Input: user question. Outputs: candidate list, filtered list, final answer. Human: requester/manager."
    },
    {
      "explanation": "3) Relation mapping: Map PERFORMS, USES, PRODUCES/CONSUMED_BY, NEXT, and DELIVERS_TO relations among entities.",
      "output": "Created PERFORMS relations for each agent→task, USES relations connecting agents to tools used (web search function, code runner, external websites). Built NEXT chain across tasks to represent sequential workflow. Mapped PRODUCES/CONSUMED_BY relations for intermediate artifacts (candidate list → filtered list → final report)."
    },
    {
      "explanation": "4) Quality check, failures and optimizations.",
      "output": "Verified relation ids reference existing entities. Noted two failures from trace (execution failure due to None result, and incorrect Python code causing wrong final answer). Added optimizations: PROMPT_REFINEMENT (explicit error-handling and None checks) and TOOL_ENHANCEMENT (robust wrapper for perform_web_search and result validation)."
    }
  ],
  "knowledge_graph": {
    "system_name": "Location-Based Eateries Verification (Multi-Agent)",
    "system_summary": "A sequential multi-agent workflow that locates Harkness Memorial State Park, searches nearby eateries, verifies operating hours (filtering for those open at 11pm on Wednesdays), and returns the closest qualifying eatery. Four specialized agents collaborate using web-search and code execution tools with intermediate verification steps.",
    "entities": [
      {
        "id": "agent_001",
        "type": "Agent",
        "name": "Computer_terminal",
        "importance": "MEDIUM",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "agent_002",
        "type": "Agent",
        "name": "DataVerification_Expert",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "agent_003",
        "type": "Agent",
        "name": "Eateries_Expert",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "agent_004",
        "type": "Agent",
        "name": "Location-Based_Services_Expert",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "task_001",
        "type": "Task",
        "name": "Identify location of Harkness Memorial State Park",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "task_002",
        "type": "Task",
        "name": "Search for nearby eateries using location-based services",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "task_003",
        "type": "Task",
        "name": "Extract and compile candidate eatery details (name, address, proximity)",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "task_004",
        "type": "Task",
        "name": "Verify operating hours (filter for open ≥ 11:00 PM on Wednesdays)",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "task_005",
        "type": "Task",
        "name": "Determine the closest eatery among filtered candidates",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "task_006",
        "type": "Task",
        "name": "Produce final report (name, address, distance, open confirmation)",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "tool_001",
        "type": "Tool",
        "name": "perform_web_search API (web search function)",
        "importance": "MEDIUM",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "tool_002",
        "type": "Tool",
        "name": "Python execution environment / code runner",
        "importance": "MEDIUM",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "tool_003",
        "type": "Tool",
        "name": "External web sources (Tripadvisor, Yelp, OpenTable)",
        "importance": "MEDIUM",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "input_001",
        "type": "Input",
        "name": "User question: closest eatery to Harkness Memorial State Park open at 11pm on Wednesdays",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "output_001",
        "type": "Output",
        "name": "Candidate eatery list (raw search results)",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "output_002",
        "type": "Output",
        "name": "Filtered eateries (open ≥ 11pm Wed)",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "output_003",
        "type": "Output",
        "name": "Final report (name, address, distance, confirmation)",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "human_001",
        "type": "Human",
        "name": "Requester / Manager",
        "importance": "HIGH",
        "raw_prompt": "",
        "raw_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      }
    ],
    "relations": [
      {
        "id": "rel_001",
        "source": "agent_004",
        "target": "task_001",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_002",
        "source": "agent_004",
        "target": "task_002",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_003",
        "source": "agent_001",
        "target": "task_002",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_004",
        "source": "agent_003",
        "target": "task_003",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_005",
        "source": "agent_002",
        "target": "task_004",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
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          }
        ]
      },
      {
        "id": "rel_006",
        "source": "agent_002",
        "target": "task_005",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
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          }
        ]
      },
      {
        "id": "rel_007",
        "source": "agent_001",
        "target": "task_004",
        "type": "PERFORMS",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_008",
        "source": "task_001",
        "target": "task_002",
        "type": "NEXT",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_009",
        "source": "task_002",
        "target": "task_003",
        "type": "NEXT",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_010",
        "source": "task_003",
        "target": "task_004",
        "type": "NEXT",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
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          }
        ]
      },
      {
        "id": "rel_011",
        "source": "task_004",
        "target": "task_005",
        "type": "NEXT",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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      },
      {
        "id": "rel_012",
        "source": "task_005",
        "target": "task_006",
        "type": "NEXT",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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        ]
      },
      {
        "id": "rel_013",
        "source": "task_002",
        "target": "output_001",
        "type": "PRODUCES",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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          }
        ]
      },
      {
        "id": "rel_014",
        "source": "output_001",
        "target": "task_003",
        "type": "CONSUMED_BY",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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        "importance": "HIGH",
        "interaction_prompt": "",
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        ]
      },
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        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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        ]
      },
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        "id": "rel_017",
        "source": "task_006",
        "target": "output_003",
        "type": "PRODUCES",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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        ]
      },
      {
        "id": "rel_018",
        "source": "output_003",
        "target": "human_001",
        "type": "DELIVERS_TO",
        "importance": "HIGH",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "rel_019",
        "source": "agent_001",
        "target": "tool_001",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
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        ]
      },
      {
        "id": "rel_020",
        "source": "agent_001",
        "target": "tool_002",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
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        ]
      },
      {
        "id": "rel_021",
        "source": "agent_004",
        "target": "tool_001",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
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        ]
      },
      {
        "id": "rel_022",
        "source": "agent_004",
        "target": "tool_003",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
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        ]
      },
      {
        "id": "rel_023",
        "source": "agent_003",
        "target": "tool_003",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
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          }
        ]
      },
      {
        "id": "rel_024",
        "source": "agent_002",
        "target": "tool_002",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
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        ]
      },
      {
        "id": "rel_025",
        "source": "agent_002",
        "target": "tool_001",
        "type": "USES",
        "importance": "MEDIUM",
        "interaction_prompt": "",
        "interaction_prompt_ref": [
          {
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        ]
      }
    ],
    "failures": [
      {
        "id": "failure_001",
        "risk_type": "EXECUTION_ERROR",
        "description": "Search function returned None and caused a TypeError when iterating results (execution failed).",
        "raw_text": "",
        "raw_text_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ],
        "affected_id": "agent_001"
      },
      {
        "id": "failure_002",
        "risk_type": "PLANNING_ERROR",
        "description": "Incorrect Python code / validation logic in DataVerification_Expert led to an incorrect final answer (trace metadata shows mistake_agent = DataVerification_Expert).",
        "raw_text": "",
        "raw_text_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ],
        "affected_id": "agent_002"
      }
    ],
    "optimizations": [
      {
        "id": "opt_001",
        "recommendation_type": "PROMPT_REFINEMENT",
        "description": "Add explicit result validation and fallback logic in DataVerification_Expert prompts and code (check for None, handle empty search results, and log partial results). This reduces EXECUTION_ERROR occurrences and clarifies error handling responsibilities.",
        "affected_ids": [
          "agent_002",
          "task_004",
          "tool_001"
        ],
        "raw_text_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      },
      {
        "id": "opt_002",
        "recommendation_type": "TOOL_ENHANCEMENT",
        "description": "Wrap perform_web_search with a robust wrapper that returns consistent structured results (list) and includes retry/backoff and provenance metadata from external sources (Yelp/Tripadvisor/OpenTable). This reduces None results and improves traceability.",
        "affected_ids": [
          "tool_001",
          "agent_001"
        ],
        "raw_text_ref": [
          {
            "line_start": null,
            "line_end": null
          }
        ]
      }
    ]
  },
  "input_trace_length": 29909,
  "input_trace_preview": "{\n  \"filename\": \"algorithm_sample_1.json\",\n  \"title\": \"Algorithm Sample 1: What is the closest eatery to Harkness Memorial State Park t...\",\n  \"description\": \"Complex location-based services sample with 4 specialized agents. Involves geographic queries, time-based filtering, and data verification.\",\n  \"trace_type\": \"location_based_services\",\n  \"trace_source\": \"algorithm_generated\",\n  \"tags\": [\n    \"multi_agent\",\n    \"algorithm_generated\",\n    \"location_services\",\n    \"data_verification\",\n    \"re..."
}