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    "headline": "The Database Asked One Question Before Naming Itself",
    "slug": "the-database-asked-one-question-before-naming-itself",
    "dek": "TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.",
    "summary": "TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.",
    "body_text": "Across seven reference databases, the system reached all expert-corrected entities and features, executed all 1,678 self-generated retrieval claims, and matched 92 to 100 percent of semantic roles; a blind ten-table test recovered the verified entity structure. The evidence is limited to the eight evaluated databases.",
    "why_it_matters": "TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.",
    "limitations": [],
    "importance": 7,
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    "tags": [
      "semantic layers",
      "databases",
      "neurosymbolic AI"
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  "sources": [
    {
      "source_id": "source-2026-08-08-018",
      "title": "arXiv preprint 2608.06331",
      "publisher": "arXiv",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "The Database Asked One Question Before Naming Itself",
    "publisher": "The Machine Press",
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