{"_id": {"$oid": "6ab1687f1481417a2aae47f4"}, "test_id": "T0315", "benchmark": "blacklist_cards", "date": "2025-10-24", "tags": {"document-type": ["index-card"], "writing": ["typed", "handwritten"], "century": [20], "language": ["de"], "entry-type": ["company"], "task": ["information-extraction"]}, "contributors": ["lea_kasper", "sorin_marti"], "hidden": false, "config": {"provider": "genai", "model": "gemini-2.5-flash", "dataclass": "Card", "temperature": 0.5, "role_description": "You are a Historian", "prompt_file": "prompt.txt", "legacy_test": false}, "prompt": "You are a meticulous archivist extracting data from an index card image. Analyze the provided image and extract the following information. Return the data ONLY as a valid JSON object.\n\n- \"company\": The primary company name, usually in the top-left. Exclude the location.\n- \"location\": The city or town, often following the company name.\n- \"b_id\": The identifier code, usually in the top-right, starting with \"B.\".\n- \"date\": Any stamped dates on the card in YYYY-MM-DD format. If no date is present, use an empty string.\n- \"information\": A list of text blocks from the main body of the card. Each block should be a separate string in the list. Maintain line breaks with \\\\n.\n\nHere is the required JSON format:\n{\n  \"company\": {\"transcription\": \"\"},\n  \"location\": {\"transcription\": \"\"},\n  \"b_id\": {\"transcription\": \"\"},\n  \"date\": \"\",\n  \"information\": [\n    {\"transcription\": \"\"}\n  ]\n}\n\nIf you cannot find a value for a field, leave its transcription value as an empty string. Do not add any explanatory text outside of the JSON object.", "results": {"provider": "genai", "model": "gemini-2.5-flash", "duration": 2.6001853942871094, "timestamp": "2025-10-24T14:26:34.617707", "parsed": {"company": {"transcription": "Abegg & Cie."}, "location": {"transcription": "Z\u00fcrich"}, "b_id": {"transcription": "B.51.322.GB.266"}, "date": null, "information": null}, "text": "{\"company\": {\"transcription\": \"Abegg & Cie.\"}, \"location\": {\"transcription\": \"Z\u00fcrich\"}, \"b_id\": {\"transcription\": \"B.51.322.GB.266\"}, \"date\": null, \"information\": null}", "raw_response": {"text": "{\"company\": {\"transcription\": \"Abegg & Cie.\"}, \"location\": {\"transcription\": \"Z\u00fcrich\"}, \"b_id\": {\"transcription\": \"B.51.322.GB.266\"}, \"date\": null, \"information\": null}", "usage_metadata": {"prompt_token_count": 523, "candidates_token_count": 59, "total_token_count": 704}}, "usage": {"input_tokens": 523, "output_tokens": 59, "total_tokens": 704, "input_cost_usd": 0.0001569, "output_cost_usd": 0.0001475, "estimated_cost_usd": 0.0006094, "reasoning_tokens": 122, "reasoning_cost_usd": 0.000305}, "finish_reason": "stop"}, "scoring": {"fuzzy": 0.8783175297669717, "cost_summary": {"total_input_tokens": 16213, "total_output_tokens": 3619, "total_tokens": 19832, "input_cost_usd": 0.004864, "output_cost_usd": 0.009047, "total_cost_usd": 0.0568639, "pricing_date": "2025-10-24", "input_price_per_million": 0.3, "output_price_per_million": 2.5, "total_reasoning_tokens": 17181, "reasoning_cost_usd": 0.0429525}}, "pricing": {"bucket_date": "2025-10-01", "age_days": 23, "input_price_per_million": 0.3, "output_price_per_million": 2.5}, "timing": {"total_response_s": 216.33, "mean_response_s": 6.98, "slowest_response_s": 17.83, "inputs_timed": 31, "inputs_stored": 33, "span_s": 261.14}, "normalized_score": 87.83175297669717}