{
    "meta_info": {
        "case_id": "Mumtaz2016_02.edf",
        "bench_subset": "NeuroBench-Core",
        "difficult": 1.5,
        "original_dataset": "Mumtaz2016"
    },
    "agent_input": {
        "data_path": "data/core/Mumtaz2016_02.edf",
        "instruction": "Please select standard EEG channels, apply common average reference, resample to 100Hz, apply a 0.5-40Hz FIR bandpass filter, and split the 30-60s segment into three 10-second windows. Compute Global Field Power (GFP) in each window and report the time point of the maximum GFP peak in each window."
    },
    "eval_config": {
        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the three GFP peak time points from the agent report:\n1) peak time in window 1\n2) peak time in window 2\n3) peak time in window 3\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text, explanations, or units.\n4. Keys must be EXACTLY \"gfp_peak_time_window_1\", \"gfp_peak_time_window_2\", and \"gfp_peak_time_window_3\".\n5. Values must be float seconds or null.\n6. If a value is missing or cannot be found, use null for that key.\n\n### OUTPUT TEMPLATE\n{\"gfp_peak_time_window_1\": <float|null>, \"gfp_peak_time_window_2\": <float|null>, \"gfp_peak_time_window_3\": <float|null>}",
        "metrics": [
            {
                "metric_id": "gfp_peak_time_window_1_accuracy",
                "type": "numeric_check",
                "target_key": "gfp_peak_time_window_1",
                "weight": 34,
                "params": {
                    "gt_value": 33.91,
                    "tolerance": 0.1
                }
            },
            {
                "metric_id": "gfp_peak_time_window_2_accuracy",
                "type": "numeric_check",
                "target_key": "gfp_peak_time_window_2",
                "weight": 33,
                "params": {
                    "gt_value": 46.87,
                    "tolerance": 0.1
                }
            },
            {
                "metric_id": "gfp_peak_time_window_3_accuracy",
                "type": "numeric_check",
                "target_key": "gfp_peak_time_window_3",
                "weight": 33,
                "params": {
                    "gt_value": 50.43,
                    "tolerance": 0.1
                }
            }
        ]
    }
}