{
    "meta_info": {
        "case_id": "case29_02_ISRUC_01",
        "bench_subset": "NeuroBench-Sleep",
        "difficult": 2,
        "original_dataset": "ISRUC"
    },
    "agent_input": {
        "data_path": "data/sleep/ISRUC_01.edf",
        "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 151.0 to 152.0, B = minute 188.0 to 189.0, C = minute 33.25 to 34.25, D = minute 234.0 to 235.0, E = minute 281.0 to 282.0. Report which segment labels contain arousal and which segment labels do not contain arousal."
    },
    "eval_config": {
        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}",
        "metrics": [
            {
                "metric_id": "segment_A_arousal_accuracy",
                "type": "categorical_check",
                "target_key": "A",
                "weight": 20,
                "params": {
                    "gt_value": "no"
                }
            },
            {
                "metric_id": "segment_B_arousal_accuracy",
                "type": "categorical_check",
                "target_key": "B",
                "weight": 20,
                "params": {
                    "gt_value": "no"
                }
            },
            {
                "metric_id": "segment_C_arousal_accuracy",
                "type": "categorical_check",
                "target_key": "C",
                "weight": 20,
                "params": {
                    "gt_value": "yes"
                }
            },
            {
                "metric_id": "segment_D_arousal_accuracy",
                "type": "categorical_check",
                "target_key": "D",
                "weight": 20,
                "params": {
                    "gt_value": "no"
                }
            },
            {
                "metric_id": "segment_E_arousal_accuracy",
                "type": "categorical_check",
                "target_key": "E",
                "weight": 20,
                "params": {
                    "gt_value": "no"
                }
            }
        ]
    }
}