{
  "anchor_concepts": [
    {
      "name": "Source provenance",
      "summary": "Every panel keeps source and source-channel context visible."
    },
    {
      "name": "Entity extraction",
      "summary": "Recurring entities and topics make news useful to models without reading every item."
    },
    {
      "name": "Time windows",
      "summary": "24h, 7d, and 30d windows help models separate new activity from background noise."
    },
    {
      "name": "Citation surface",
      "summary": "JSON twins give LLMs a stable public reference layer."
    }
  ],
  "boundary": "public_reference_page_not_retrieval_api",
  "canonical_url": "https://hangingcontext.com/definitions/structured-news-for-llms/",
  "generated_at": "2026-05-20T12:17:55+00:00",
  "html_url": "https://hangingcontext.com/definitions/structured-news-for-llms/",
  "json_url": "https://hangingcontext.com/data/definitions/structured-news-for-llms.json",
  "page_type": "definition_citation_page",
  "related_hc_surfaces": [
    "/sources/",
    "/streams/",
    "/tags/",
    "/mcp/"
  ],
  "schema_version": 1,
  "summary": "Structured news for LLMs turns changing public information into citable signals with source, entity, timestamp, topic, and claim structure.",
  "target_queries": [
    "structured news for LLMs",
    "news feed for LLM",
    "AI structured news data"
  ],
  "term": "Structured news for LLMs"
}