{"_id": {"$oid": "69d4eb12b877594bc5718c9a"}, "test_id": "T0629", "benchmark": "business_letters", "date": "2026-03-16", "tags": {"document-type": ["letter"], "writing": ["typed", "handwritten"], "century": [20], "language": ["de"], "layout": ["prose"], "entry-type": ["person", "location"], "task": ["information-extraction"]}, "contributors": ["eric_decker", "maximilian_hindermann", "lea_kasper", "anthea_alberto", "pema_frick", "jose_luis_losada_palenzuela", "sorin_marti", "elena_spadini"], "hidden": false, "config": {"provider": "anthropic", "model": "claude-opus-4-6", "dataclass": "Document", "temperature": 0.0, "role_description": "You are a historian with keyword knowledge and an expert in the field of 20th century Swiss history", "prompt_file": "prompt.txt", "legacy_test": false}, "prompt": "            IDENTITY and PURPOSE:\n\n            You are presented with a series of images constituting a historical letter. Your task is to extract the\n            values of the following keys from the letter and return them in a JSON file where the values\n            corresponding to each key should be stored as a list, even if there is only a single value for a\n            key:\n\n            - letter_title: Title of the letter.\n            - sender_persons: Name(s) of the person(s) who wrote the letter.\n            - send_date: The exact or approximate date the letter was written.\n            - receiver_persons: Name(s) of the person(s) who received the letter.\n\n            Take a deep breath and think step by step about how to best accomplish this goal. Map out all the claims\n            and implications on a virtual whiteboard in your mind. Do not use OCR. Use the ISO format YYYY-MM-DD for\n            dates. If a piece of information is not included in the letter, set the value for the corresponding key\n            to \"null\". Do not return anything except the JSON file.\n\n            EXAMPLE:\n\n            {\n                \"letter_title\": [\"Petition for Environmental Protection\"],\n                \"send_date\": [\"1993-03-12\"],\n                \"sender_persons\": [\"Lisa Simpson\"],\n                \"receiver_persons\": [\"Mayor Joe Quimby\", \"Seymour Skinner\"],\n            }\n\n            OUTPUT:", "results": {"text": "{\"metadata\":{\"send_date\":[\"1926-06-28\"],\"letter_title\":null,\"sender_persons\":[\"Max Vischer\"],\"receiver_persons\":[\"P. Christ-Wackernagel\"]}}", "model": "claude-opus-4-6", "provider": "anthropic", "finish_reason": "tool_use", "usage": {"input_tokens": 4342, "output_tokens": 80, "total_tokens": 4422, "input_cost_usd": 0.02171, "output_cost_usd": 0.002, "estimated_cost_usd": 0.023710000000000002}, "duration": 5.739343166351318, "timestamp": "2026-03-16T15:49:46.261158", "parsed": {"metadata": {"send_date": ["1926-06-28"], "letter_title": null, "sender_persons": ["Max Vischer"], "receiver_persons": ["P. Christ-Wackernagel"], "document_number": "letter09"}}, "conversation_id": "d9cba67d-01ef-4add-b612-4b8f0d5cb268", "score": {"send_date_tp": 1, "send_date_fp": 0, "send_date_fn": 0, "sender_persons_tp": 1, "sender_persons_fp": 0, "sender_persons_fn": 0, "receiver_persons_tp": 1, "receiver_persons_fp": 0, "receiver_persons_fn": 0}, "raw_response": "{\"id\":\"msg_016V3KGw4GLydfPnP1BmhKyE\",\"content\":[{\"id\":\"toolu_019K7dswDdirGosxC3yXtaPS\",\"input\":{\"metadata\":{\"send_date\":[\"1926-06-28\"],\"letter_title\":null,\"sender_persons\":[\"Max Vischer\"],\"receiver_persons\":[\"P. Christ-Wackernagel\"],\"document_number\":\"letter09\"}},\"name\":\"extract_structured_data\",\"type\":\"tool_use\",\"caller\":{\"type\":\"direct\"}}],\"model\":\"claude-opus-4-6\",\"role\":\"assistant\",\"stop_reason\":\"tool_use\",\"stop_sequence\":null,\"type\":\"message\",\"usage\":{\"cache_creation\":{\"ephemeral_1h_input_tokens\":0,\"ephemeral_5m_input_tokens\":0},\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"inference_geo\":\"global\",\"input_tokens\":4342,\"output_tokens\":80,\"server_tool_use\":null,\"service_tier\":\"standard\"}}"}, "scoring": {"f1_macro": 0.58, "f1_micro": 0.57, "cost_summary": {"total_input_tokens": 222630, "total_output_tokens": 5656, "total_tokens": 228286, "input_cost_usd": 1.1131499999999996, "output_cost_usd": 0.14139999999999994, "total_cost_usd": 1.2545500000000003}}, "normalized_score": 57.99999999999999}