{"_id": {"$oid": "69d4eb12b877594bc5718c1f"}, "test_id": "T0493", "benchmark": "library_cards", "date": "2026-01-26", "tags": {"document-type": ["index-card"], "writing": ["typed", "printed", "handwritten"], "century": [20, 19], "language": ["de", "fr", "en", "la", "el", "fi", "sv", "pl"], "layout": ["index"], "entry-type": ["bibliographic"], "task": ["information-extraction"]}, "contributors": ["gabriel_muller", "maximilian_hindermann"], "hidden": false, "config": {"provider": "openai", "model": "gpt-5.2-2025-12-11", "dataclass": "Document", "temperature": 0.0, "role_description": "You are a historian with keyword knowledge", "prompt_file": "prompt.txt", "legacy_test": false}, "prompt": "Extract the bibliographic information about a historical dissertation from this index card and return it as a structured JSON object with the following exact format:\n\n```json\n{\n  \"type\": {\n    \"type\": \"Dissertation or thesis\" OR \"Reference\"\n  },\n  \"author\": {\n    \"last_name\": \"string\",\n    \"first_name\": \"string\"\n  },\n  \"publication\": {\n    \"title\": \"string\",\n    \"year\": integer,\n    \"place\": \"string or empty string\",\n    \"pages\": \"string or empty string\",\n    \"publisher\": \"string or empty string\",\n    \"format\": \"string or empty string\"\n  },\n  \"library_reference\": {\n    \"shelfmark\": \"string or empty string\",\n    \"subjects\": \"string or empty string\"\n  }\n}\n```\n\nEXTRACTION RULES:\n1. **Card Type**: If a card contains the note \"s.\" on a separate line, it is a \"Reference\". Otherwise, it is a \"Dissertation or thesis\".\n\n2. **Author**: Extract last_name and first_name. If only one name is given, put it in last_name and leave first_name empty.\n\n3. **Publication**:\n   - title: The main title of the work\n   - year: Publication year as integer\n   - place: Publication place\n   - pages: Page count (remove \" S.\" suffix if present)\n   - publisher: Publishing house/institution\n   - format: Usually \"8\u00b0\", \"8'\", or \"4\u00b0\" - single value only\n\n4. **Library Reference**:\n   - shelfmark: Often begins with \"Diss.\" or \"AT\", may be marked with \"Standort:\"\n   - subjects: Subject classifications or keywords\n\n5. **Missing Information**: Use empty string \"\" for missing text fields, omit year fields entirely if not present.\n\n6. **Ignore**: Disregard any information that doesn't fit into these categories.\n\nReturn ONLY the JSON object, no additional text or explanation.", "results": {"text": "", "model": "gpt-5.2", "provider": "openai", "finish_reason": "error", "usage": {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}, "duration": 22.381165027618408, "timestamp": "2026-01-27T00:19:36.060951", "score": {"f1_score": 0.0, "precision": 0.0, "recall": 0.0, "true_positives": 0, "false_positives": 0, "false_negatives": 8, "field_scores": {"": {"response": null, "ground_truth": null, "score": 1.0}, "author.last_name": {"response": null, "ground_truth": "Worms", "score": 0.0}, "publication.title": {"response": null, "ground_truth": "De natura et methodo Sociologi\u00e6", "score": 0.0}, "type.type": {"response": null, "ground_truth": "Dissertation or thesis", "score": 0.0}, "publication.year": {"response": null, "ground_truth": 1896, "score": 0.0}, "library_reference.subjects": {"response": null, "ground_truth": null, "score": 1.0}, "publication.publisher": {"response": null, "ground_truth": null, "score": 1.0}, "publication.pages": {"response": null, "ground_truth": "103", "score": 0.0}, "library_reference.shelfmark": {"response": null, "ground_truth": null, "score": 1.0}, "author.first_name": {"response": null, "ground_truth": "Ren\u00e9", "score": 0.0}, "publication.format": {"response": null, "ground_truth": "8\u00b0", "score": 0.0}, "publication.place": {"response": null, "ground_truth": "Lutetia Parisiorum", "score": 0.0}}, "total_fields": 12}}, "scoring": {"f1_micro": 0.46877774489493934, "f1_macro": 0.32532319391634984, "micro_precision": 0.8215767634854771, "micro_recall": 0.3279503105590062, "total_instances": 263, "total_tp": 792, "total_fp": 172, "total_fn": 1623, "cost_summary": {"total_input_tokens": 185513, "total_output_tokens": 55962, "total_tokens": 241475, "input_cost_usd": 0.3246477499999993, "output_cost_usd": 0.7834679999999998, "total_cost_usd": 1.1081157500000003}}, "normalized_score": 32.532319391634985}