{
    "meta_info":  {
                      "case_id":  "ISRUC_01.edf",
                      "bench_subset":  "NeuroBench-Core",
                      "difficult": 1.0,
                      "original_dataset":  "ISRUC"
                  },
    "agent_input":  {
                        "data_path":  "data/core/ISRUC_01.edf",
                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the 110-120 minute segment and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
                    },
    "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 two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\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 or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
                        "metrics":  [
                                        {
                                            "metric_id":  "dominant_band_accuracy",
                                            "type":  "categorical_check",
                                            "target_key":  "dominant_band",
                                            "weight":  70,
                                            "params":  {
                                                           "gt_value":  "delta"
                                                       }
                                        },
                                        {
                                            "metric_id":  "secondary_band_accuracy",
                                            "type":  "categorical_check",
                                            "target_key":  "secondary_band",
                                            "weight":  30,
                                            "params":  {
                                                           "gt_value":  "theta"
                                                       }
                                        }
                                    ]
                    }
}