{"_id": {"$oid": "6a1010f996d87b97b14cf893"}, "test_id": "T0955", "benchmark": "personnel_cards", "date": "2026-04-21", "tags": {"document-type": ["index-card"], "writing": ["handwritten", "typed", "printed"], "century": [20], "layout": ["table", "form"], "task": ["transcription", "document-understanding", "data-correction"], "language": ["de", "fr"]}, "contributors": ["tabea_wullschleger", "maximilian_hindermann"], "hidden": false, "config": {"provider": "openrouter", "model": "qwen/qwen3.5-397b-a17b", "dataclass": "Table", "temperature": 0.0, "role_description": "You are a historian specializing in 20th century personnel records and administrative documents", "prompt_file": "prompt.txt", "legacy_test": false}, "prompt": "Extract all information from this personnel card table and return it as a structured JSON object. The card contains rows documenting employment history with positions, locations, salary information, dates, and remarks.\n\nREQUIRED JSON STRUCTURE:\n```json\n{\n  \"rows\": [\n    {\n      \"row_number\": 1,\n      \"dienstliche_stellung\": {\n        \"diplomatic_transcript\": \"exact text from card\",\n        \"interpretation\": \"expanded/standardized form or null\",\n        \"is_crossed_out\": false\n      },\n      \"dienstort\": {\n        \"diplomatic_transcript\": \"exact text from card\",\n        \"interpretation\": \"expanded/standardized form or null\",\n        \"is_crossed_out\": false\n      },\n      \"gehaltsklasse\": {\n        \"diplomatic_transcript\": \"exact text from card\",\n        \"interpretation\": \"expanded/standardized form or null\",\n        \"is_crossed_out\": false\n      },\n      \"jahresgehalt_monatsgehalt_taglohn\": {\n        \"diplomatic_transcript\": \"exact text from card\",\n        \"interpretation\": \"standardized numeric form or null\",\n        \"is_crossed_out\": false\n      },\n      \"datum_gehalts\u00e4nderung\": {\n        \"diplomatic_transcript\": \"exact text from card\",\n        \"interpretation\": \"YYYY-MM-DD format or null\",\n        \"is_crossed_out\": false\n      },\n      \"bemerkungen\": {\n        \"diplomatic_transcript\": \"exact text from card\",\n        \"interpretation\": \"expanded/standardized form or null\",\n        \"is_crossed_out\": false\n      }\n    }\n  ]\n}\n```\n\nCOLUMN DEFINITIONS:\n1. **dienstliche_stellung**: Official position or job title (e.g., \"Assistent\", \"Professor\", \"Sekret\u00e4r\")\n2. **dienstort**: Place of service or work location (e.g., \"Basel\", \"Z\u00fcrich\")\n3. **gehaltsklasse**: Salary class or grade (e.g., \"III\", \"IV\", roman numerals or numbers)\n4. **jahresgehalt_monatsgehalt_taglohn**: Salary amount (annual/monthly/daily wage)\n5. **datum_gehalts\u00e4nderung**: Date of salary change or effective date\n6. **bemerkungen**: Remarks, notes, or additional comments\n\nFIELD EXTRACTION RULES:\n\n**diplomatic_transcript** (REQUIRED for all fields):\n- Transcribe EXACTLY as written on the card\n- Include all abbreviations, punctuation, and formatting as they appear\n- Preserve original capitalization and spacing\n- Include currency symbols and separators (e.g., \"Fr. 2'400.-\", \"3.700.-\")\n- For dates, copy the exact format (e.g., \"1. Jan. 1946\", \"1.April 1945\")\n- Use empty string \"\" for empty cells\n- Be sure to escape ditto marks (repetition marks such as `\"` indicating \"same as above\")\n- DO NOT expand abbreviations or standardize formats\n- Do not transcribe checkmarks\n\n**interpretation** (OPTIONAL - use null if not applicable):\n- For ditto marks (repetition marks such as `\"`): Replace with the actual repeated value from the previous row in the same column\n  - For example, if previous row's dienstliche_stellung is \"Hilfsleiterin\" and current row has `\"`, interpret as \"Hilfsleiterin\"\n  - Do NOT add explanatory text like \"wie oben\" or similar - just provide the repeated value\n- Never expand abbreviations\n- For dates: Convert to ISO format YYYY-MM-DD\n  - \"1. Jan. 1946\" \u2192 \"1946-01-01\"\n  - \"1.April 1945\" \u2192 \"1945-04-01\"\n- For salary amounts: Extract numeric value only (remove currency symbols, separators)\n  - \"Fr. 2'400.-\" \u2192 \"2400\"\n  - \"3.700.-\" \u2192 \"3700\"\n  - \"5.094.-\" \u2192 \"5094\"\n  \"6,30.-\" \u2192 \"6.3\"\n- For salary class: Convert roman numerals to arabic if clear\n  - \"III\" \u2192 \"3\"\n  - \"IV\" \u2192 \"4\"\n- Do not convert roman numerals to arabic if not salary, date, or salary class\n- Use null if no interpretation/expansion is needed or if the field is empty\n- \"+ {word}\" where {word} is a word does not need interpretation\n\n**is_crossed_out** (REQUIRED for all fields):\n- Set to true if text in this field is crossed out, struck through, or deleted\n- Set to false if text is normal (not crossed out)\n- Empty cells should have is_crossed_out: false\n\nROW HANDLING:\n- Number rows sequentially starting from 1 (top to bottom)\n- Include ALL rows that have ANY content in ANY column\n- Empty rows (all cells empty) should be omitted\n- A row with only one filled cell should still be included\n\nIMPORTANT NOTES:\n- Return ONLY the JSON object, no additional text or explanation\n- Every field must have all three sub-fields: diplomatic_transcript, interpretation, is_crossed_out\n- diplomatic_transcript must never be null (use empty string \"\" for empty cells)\n- interpretation can be null when no expansion/standardization applies\n- Process the entire table from top to bottom\n- Maintain consistent row numbering throughout\n", "results": {"text": "{\"rows\":[{\"row_number\":1,\"dienstliche_stellung\":{\"diplomatic_transcript\":\"Hilfsleiterin\",\"interpretation\":null,\"is_crossed_out\":false},\"dienstort\":{\"diplomatic_transcript\":\"\",\"interpretation\":null,\"is_crossed_out\":false},\"gehaltsklasse\":{\"diplomatic_transcript\":\"\",\"interpretation\":null,\"is_crossed_out\":false},\"jahresgehalt_monatsgehalt_taglohn\":{\"diplomatic_transcript\":\"4200.-\",\"interpretation\":\"4200\",\"is_crossed_out\":false},\"datum_gehalts\u00e4nderung\":{\"diplomatic_transcript\":\"9. Okt. 45\",\"interpretation\":\"1945-10-09\",\"is_crossed_out\":false},\"bemerkungen\":{\"diplomatic_transcript\":\"+ Zulagen\",\"interpretation\":null,\"is_crossed_out\":false}}]}", "model": "qwen/qwen3.5-397b-a17b-20260216", "provider": "openrouter", "finish_reason": "stop", "usage": {"input_tokens": 4230, "output_tokens": 1702, "total_tokens": 5932, "input_cost_usd": 0.0016497000000000003, "output_cost_usd": 0.00398268, "estimated_cost_usd": 0.00563238}, "duration": 33.25319814682007, "timestamp": "2026-04-21T17:20:53.909841", "parsed": {"rows": [{"row_number": 1, "dienstliche_stellung": {"diplomatic_transcript": "Hilfsleiterin", "interpretation": null, "is_crossed_out": false}, "dienstort": {"diplomatic_transcript": "", "interpretation": null, "is_crossed_out": false}, "gehaltsklasse": {"diplomatic_transcript": "", "interpretation": null, "is_crossed_out": false}, "jahresgehalt_monatsgehalt_taglohn": {"diplomatic_transcript": "4200.-", "interpretation": "4200", "is_crossed_out": false}, "datum_gehalts\u00e4nderung": {"diplomatic_transcript": "9. Okt. 45", "interpretation": "1945-10-09", "is_crossed_out": false}, "bemerkungen": {"diplomatic_transcript": "+ Zulagen", "interpretation": null, "is_crossed_out": false}}]}, "conversation_id": "360459c2-0f21-45b4-bf28-9aa40dd80e02", "score": {"f1_score": 1.0, "precision": 1.0, "recall": 1.0, "true_positives": 12, "false_positives": 0, "false_negatives": 0, "field_scores": {"rows[0].datum_gehalts\u00e4nderung.diplomatic_transcript": {"response": "9. Okt. 45", "ground_truth": "9. Okt. 45", "score": 1.0}, "rows[0].gehaltsklasse.diplomatic_transcript": {"response": null, "ground_truth": null, "score": 1.0}, "rows[0].bemerkungen.diplomatic_transcript": {"response": "+ Zulagen", "ground_truth": "+ Zulagen", "score": 1.0}, "rows[0].dienstliche_stellung.interpretation": {"response": null, "ground_truth": null, "score": 1.0}, "rows[0].bemerkungen.is_crossed_out": {"response": false, "ground_truth": false, "score": 1.0}, "rows[0].gehaltsklasse.interpretation": {"response": null, "ground_truth": null, "score": 1.0}, "rows[0].jahresgehalt_monatsgehalt_taglohn.diplomatic_transcript": {"response": "4200.-", "ground_truth": "4200.-", "score": 1.0}, "rows[0].gehaltsklasse.is_crossed_out": {"response": false, "ground_truth": false, "score": 1.0}, "rows[0].bemerkungen.interpretation": {"response": null, "ground_truth": null, "score": 1.0}, "rows[0].jahresgehalt_monatsgehalt_taglohn.interpretation": {"response": "4200", "ground_truth": "4200", "score": 1.0}, "rows[0].dienstort.diplomatic_transcript": {"response": null, "ground_truth": null, "score": 1.0}, "rows[0].jahresgehalt_monatsgehalt_taglohn.is_crossed_out": {"response": false, "ground_truth": false, "score": 1.0}, "rows[0].datum_gehalts\u00e4nderung.interpretation": {"response": "1945-10-09", "ground_truth": "1945-10-09", "score": 1.0}, "rows[0].dienstliche_stellung.is_crossed_out": {"response": false, "ground_truth": false, "score": 1.0}, "rows[0].datum_gehalts\u00e4nderung.is_crossed_out": {"response": false, "ground_truth": false, "score": 1.0}, "rows[0].dienstort.is_crossed_out": {"response": false, "ground_truth": false, "score": 1.0}, "rows[0].dienstort.interpretation": {"response": null, "ground_truth": null, "score": 1.0}, "rows[0].dienstliche_stellung.diplomatic_transcript": {"response": "Hilfsleiterin", "ground_truth": "Hilfsleiterin", "score": 1.0}}, "total_fields": 19}, "raw_response": "{\"id\":\"gen-1776784820-nvex0szVWqGm2NUHUV7y\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"logprobs\":null,\"message\":{\"content\":\"{\\n  \\\"rows\\\": [\\n    {\\n      \\\"row_number\\\": 1,\\n      \\\"dienstliche_stellung\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"Hilfsleiterin\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"dienstort\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"gehaltsklasse\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"jahresgehalt_monatsgehalt_taglohn\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"4200.-\\\",\\n        \\\"interpretation\\\": \\\"4200\\\",\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"datum_gehalts\u00e4nderung\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"9. Okt. 45\\\",\\n        \\\"interpretation\\\": \\\"1945-10-09\\\",\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"bemerkungen\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"+ Zulagen\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      }\\n    }\\n  ]\\n}\",\"refusal\":null,\"role\":\"assistant\",\"annotations\":null,\"audio\":null,\"function_call\":null,\"tool_calls\":null,\"parsed\":{\"rows\":[{\"row_number\":1,\"dienstliche_stellung\":{\"diplomatic_transcript\":\"Hilfsleiterin\",\"interpretation\":null,\"is_crossed_out\":false},\"dienstort\":{\"diplomatic_transcript\":\"\",\"interpretation\":null,\"is_crossed_out\":false},\"gehaltsklasse\":{\"diplomatic_transcript\":\"\",\"interpretation\":null,\"is_crossed_out\":false},\"jahresgehalt_monatsgehalt_taglohn\":{\"diplomatic_transcript\":\"4200.-\",\"interpretation\":\"4200\",\"is_crossed_out\":false},\"datum_gehalts\u00e4nderung\":{\"diplomatic_transcript\":\"9. Okt. 45\",\"interpretation\":\"1945-10-09\",\"is_crossed_out\":false},\"bemerkungen\":{\"diplomatic_transcript\":\"+ Zulagen\",\"interpretation\":null,\"is_crossed_out\":false}}]},\"reasoning\":\"The user wants me to extract data from a personnel card image and format it as a specific JSON structure.\\n\\n**1. Analyze the Image:**\\n- The image is a personnel card with columns:\\n    - Dienstliche Stellung (Official Position)\\n    - Dienstort (Place of Service)\\n    - Gehaltsklasse (Salary Class)\\n    - Jahresgehalt Monatsgehalt Taglohn (Annual/Monthly/Daily Salary)\\n    - Zeitpunkt der Gehalts\u00e4nderung (Date of Salary Change) - split into Tag (Day), Monat (Month), Jahr (Year)\\n    - Bemerkungen, Akten etc. (Remarks)\\n\\n- There is only one row of handwritten/typed data visible.\\n- **Row 1 Content:**\\n    - Dienstliche Stellung: \\\"Hilfsleiterin\\\"\\n    - Dienstort: Empty\\n    - Gehaltsklasse: Empty\\n    - Jahresgehalt...: \\\"4200.-\\\"\\n    - Zeitpunkt...: \\\"9.\\\" (Tag), \\\"Okt.\\\" (Monat), \\\"45\\\" (Jahr) -> Combined date: 9. Okt. 45\\n    - Bemerkungen: \\\"+ Zulagen\\\"\\n\\n**2. Map to JSON Structure:**\\n\\n*   **Row 1:**\\n    *   `dienstliche_stellung`:\\n        *   `diplomatic_transcript`: \\\"Hilfsleiterin\\\"\\n        *   `interpretation`: null (no abbreviation to expand, though it's a title)\\n        *   `is_crossed_out`: false\\n    *   `dienstort`:\\n        *   `diplomatic_transcript`: \\\"\\\"\\n        *   `interpretation`: null\\n        *   `is_crossed_out`: false\\n    *   `gehaltsklasse`:\\n        *   `diplomatic_transcript`: \\\"\\\"\\n        *   `interpretation`: null\\n        *   `is_crossed_out`: false\\n    *   `jahresgehalt_monatsgehalt_taglohn`:\\n        *   `diplomatic_transcript`: \\\"4200.-\\\"\\n        *   `interpretation`: \\\"4200\\\" (numeric value)\\n        *   `is_crossed_out`: false\\n    *   `datum_gehalts\u00e4nderung`:\\n        *   `diplomatic_transcript`: \\\"9. Okt. 45\\\" (Combining the three sub-columns into one string as per standard transcription practice for dates, or keeping them separate? The prompt asks for \\\"datum_gehalts\u00e4nderung\\\" as a single field in the JSON structure, but the card has 3 sub-columns. I will combine them into the transcript string \\\"9. Okt. 45\\\" to represent the full date entry).\\n        *   `interpretation`: \\\"1945-10-09\\\" (ISO format)\\n        *   `is_crossed_out`: false\\n    *   `bemerkungen`:\\n        *   `diplomatic_transcript`: \\\"+ Zulagen\\\"\\n        *   `interpretation`: null (or maybe \\\"plus allowances\\\" but instructions say \\\"Never expand abbreviations\\\" and \\\"+ Zulagen\\\" is quite clear. I'll stick to null or just the text if it's not an abbreviation. Actually, \\\"+ Zulagen\\\" is standard German. I will leave interpretation as null unless it's an abbreviation like \\\"Dr.\\\" -> \\\"Doktor\\\". Here it's just text.)\\n        *   `is_crossed_out`: false\\n\\n**3. Refining the Date Transcription:**\\nThe card has columns for Tag, Monat, Jahr.\\nTranscript: \\\"9. Okt. 45\\\"\\nInterpretation: \\\"1945-10-09\\\"\\n\\n**4. Refining Salary Transcription:**\\nTranscript: \\\"4200.-\\\"\\nInterpretation: \\\"4200\\\"\\n\\n**5. Final Check of JSON Structure:**\\n- Ensure all keys are present.\\n- Ensure `diplomatic_transcript` is never null.\\n- Ensure `is_crossed_out` is boolean.\\n\\nLet's construct the JSON.\\n\\n```json\\n{\\n  \\\"rows\\\": [\\n    {\\n      \\\"row_number\\\": 1,\\n      \\\"dienstliche_stellung\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"Hilfsleiterin\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"dienstort\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"gehaltsklasse\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"jahresgehalt_monatsgehalt_taglohn\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"4200.-\\\",\\n        \\\"interpretation\\\": \\\"4200\\\",\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"datum_gehalts\u00e4nderung\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"9. Okt. 45\\\",\\n        \\\"interpretation\\\": \\\"1945-10-09\\\",\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"bemerkungen\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"+ Zulagen\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      }\\n    }\\n  ]\\n}\\n```\\nWait, looking closely at the date columns.\\nTag: \\\"9.\\\"\\nMonat: \\\"Okt.\\\"\\nJahr: \\\"45\\\"\\nI will combine these into the transcript for `datum_gehalts\u00e4nderung`.\\n\\nOne detail: \\\"Hilfsleiterin\\\" is a compound word.\\nOne detail: \\\"+ Zulagen\\\" - interpretation could be null.\\n\\nLet's double check the `interpretation` rules.\\n\\\"For salary amounts: Extract numeric value only (remove currency symbols, separators)\\\" -> \\\"4200\\\"\\n\\\"For dates: Convert to ISO format YYYY-MM-DD\\\" -> \\\"1945-10-09\\\"\\n\\nThe image shows only one row of data. The rest of the card is empty grid lines.\\n\\nFinal verification of the transcript text:\\n\\\"Hilfsleiterin\\\" - correct.\\n\\\"4200.-\\\" - correct.\\n\\\"9.\\\" \\\"Okt.\\\" \\\"45\\\" - correct.\\n\\\"+ Zulagen\\\" - correct.\\n\\nThe JSON structure looks solid.\",\"reasoning_details\":[{\"type\":\"reasoning.text\",\"text\":\"The user wants me to extract data from a personnel card image and format it as a specific JSON structure.\\n\\n**1. Analyze the Image:**\\n- The image is a personnel card with columns:\\n    - Dienstliche Stellung (Official Position)\\n    - Dienstort (Place of Service)\\n    - Gehaltsklasse (Salary Class)\\n    - Jahresgehalt Monatsgehalt Taglohn (Annual/Monthly/Daily Salary)\\n    - Zeitpunkt der Gehalts\u00e4nderung (Date of Salary Change) - split into Tag (Day), Monat (Month), Jahr (Year)\\n    - Bemerkungen, Akten etc. (Remarks)\\n\\n- There is only one row of handwritten/typed data visible.\\n- **Row 1 Content:**\\n    - Dienstliche Stellung: \\\"Hilfsleiterin\\\"\\n    - Dienstort: Empty\\n    - Gehaltsklasse: Empty\\n    - Jahresgehalt...: \\\"4200.-\\\"\\n    - Zeitpunkt...: \\\"9.\\\" (Tag), \\\"Okt.\\\" (Monat), \\\"45\\\" (Jahr) -> Combined date: 9. Okt. 45\\n    - Bemerkungen: \\\"+ Zulagen\\\"\\n\\n**2. Map to JSON Structure:**\\n\\n*   **Row 1:**\\n    *   `dienstliche_stellung`:\\n        *   `diplomatic_transcript`: \\\"Hilfsleiterin\\\"\\n        *   `interpretation`: null (no abbreviation to expand, though it's a title)\\n        *   `is_crossed_out`: false\\n    *   `dienstort`:\\n        *   `diplomatic_transcript`: \\\"\\\"\\n        *   `interpretation`: null\\n        *   `is_crossed_out`: false\\n    *   `gehaltsklasse`:\\n        *   `diplomatic_transcript`: \\\"\\\"\\n        *   `interpretation`: null\\n        *   `is_crossed_out`: false\\n    *   `jahresgehalt_monatsgehalt_taglohn`:\\n        *   `diplomatic_transcript`: \\\"4200.-\\\"\\n        *   `interpretation`: \\\"4200\\\" (numeric value)\\n        *   `is_crossed_out`: false\\n    *   `datum_gehalts\u00e4nderung`:\\n        *   `diplomatic_transcript`: \\\"9. Okt. 45\\\" (Combining the three sub-columns into one string as per standard transcription practice for dates, or keeping them separate? The prompt asks for \\\"datum_gehalts\u00e4nderung\\\" as a single field in the JSON structure, but the card has 3 sub-columns. I will combine them into the transcript string \\\"9. Okt. 45\\\" to represent the full date entry).\\n        *   `interpretation`: \\\"1945-10-09\\\" (ISO format)\\n        *   `is_crossed_out`: false\\n    *   `bemerkungen`:\\n        *   `diplomatic_transcript`: \\\"+ Zulagen\\\"\\n        *   `interpretation`: null (or maybe \\\"plus allowances\\\" but instructions say \\\"Never expand abbreviations\\\" and \\\"+ Zulagen\\\" is quite clear. I'll stick to null or just the text if it's not an abbreviation. Actually, \\\"+ Zulagen\\\" is standard German. I will leave interpretation as null unless it's an abbreviation like \\\"Dr.\\\" -> \\\"Doktor\\\". Here it's just text.)\\n        *   `is_crossed_out`: false\\n\\n**3. Refining the Date Transcription:**\\nThe card has columns for Tag, Monat, Jahr.\\nTranscript: \\\"9. Okt. 45\\\"\\nInterpretation: \\\"1945-10-09\\\"\\n\\n**4. Refining Salary Transcription:**\\nTranscript: \\\"4200.-\\\"\\nInterpretation: \\\"4200\\\"\\n\\n**5. Final Check of JSON Structure:**\\n- Ensure all keys are present.\\n- Ensure `diplomatic_transcript` is never null.\\n- Ensure `is_crossed_out` is boolean.\\n\\nLet's construct the JSON.\\n\\n```json\\n{\\n  \\\"rows\\\": [\\n    {\\n      \\\"row_number\\\": 1,\\n      \\\"dienstliche_stellung\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"Hilfsleiterin\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"dienstort\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"gehaltsklasse\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"jahresgehalt_monatsgehalt_taglohn\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"4200.-\\\",\\n        \\\"interpretation\\\": \\\"4200\\\",\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"datum_gehalts\u00e4nderung\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"9. Okt. 45\\\",\\n        \\\"interpretation\\\": \\\"1945-10-09\\\",\\n        \\\"is_crossed_out\\\": false\\n      },\\n      \\\"bemerkungen\\\": {\\n        \\\"diplomatic_transcript\\\": \\\"+ Zulagen\\\",\\n        \\\"interpretation\\\": null,\\n        \\\"is_crossed_out\\\": false\\n      }\\n    }\\n  ]\\n}\\n```\\nWait, looking closely at the date columns.\\nTag: \\\"9.\\\"\\nMonat: \\\"Okt.\\\"\\nJahr: \\\"45\\\"\\nI will combine these into the transcript for `datum_gehalts\u00e4nderung`.\\n\\nOne detail: \\\"Hilfsleiterin\\\" is a compound word.\\nOne detail: \\\"+ Zulagen\\\" - interpretation could be null.\\n\\nLet's double check the `interpretation` rules.\\n\\\"For salary amounts: Extract numeric value only (remove currency symbols, separators)\\\" -> \\\"4200\\\"\\n\\\"For dates: Convert to ISO format YYYY-MM-DD\\\" -> \\\"1945-10-09\\\"\\n\\nThe image shows only one row of data. The rest of the card is empty grid lines.\\n\\nFinal verification of the transcript text:\\n\\\"Hilfsleiterin\\\" - correct.\\n\\\"4200.-\\\" - correct.\\n\\\"9.\\\" \\\"Okt.\\\" \\\"45\\\" - correct.\\n\\\"+ Zulagen\\\" - correct.\\n\\nThe JSON structure looks solid.\",\"format\":\"unknown\",\"index\":0}]},\"native_finish_reason\":\"stop\"}],\"created\":1776784820,\"model\":\"qwen/qwen3.5-397b-a17b-20260216\",\"object\":\"chat.completion\",\"service_tier\":null,\"system_fingerprint\":null,\"usage\":{\"completion_tokens\":1702,\"prompt_tokens\":4230,\"total_tokens\":5932,\"completion_tokens_details\":{\"accepted_prediction_tokens\":null,\"audio_tokens\":0,\"reasoning_tokens\":1247,\"rejected_prediction_tokens\":null,\"image_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cached_tokens\":0,\"cache_write_tokens\":0,\"video_tokens\":0},\"cost\":0.0086652,\"is_byok\":false,\"cost_details\":{\"upstream_inference_cost\":0.0086652,\"upstream_inference_prompt_cost\":0.002538,\"upstream_inference_completions_cost\":0.0061272}},\"provider\":\"Together\"}"}, "scoring": {"f1_micro": 0.9185586336179963, "f1_macro": 0.8634426229508197, "micro_precision": 0.9658344283837057, "micro_recall": 0.8756949960285941, "total_instances": 61, "total_tp": 2205, "total_fp": 78, "total_fn": 313, "cost_summary": {"total_input_tokens": 270759, "total_output_tokens": 189894, "total_tokens": 460653, "input_cost_usd": 0.10559600999999996, "output_cost_usd": 0.44435196, "total_cost_usd": 0.5499479700000001}}, "normalized_score": 91.85586336179963}