{
  "anchor_concepts": [
    {
      "name": "Time-aware edges",
      "summary": "Context changes, so edges carry observed windows, recency, and freshness state."
    },
    {
      "name": "Entity and source grounding",
      "summary": "People, organizations, places, topics, data tags, and sources are stable graph nodes."
    },
    {
      "name": "Claims, signals, and briefs",
      "summary": "Atomic claims and derived signals make the graph explainable without exposing raw retrieval."
    },
    {
      "name": "Panel snapshots",
      "summary": "Public panels expose enough aggregate shape for citation while keeping item-level retrieval in Synorb."
    }
  ],
  "boundary": "public_reference_page_not_retrieval_api",
  "canonical_url": "https://hangingcontext.com/definitions/temporal-context-graph/",
  "generated_at": "2026-05-20T12:17:55+00:00",
  "html_url": "https://hangingcontext.com/definitions/temporal-context-graph/",
  "json_url": "https://hangingcontext.com/data/definitions/temporal-context-graph.json",
  "page_type": "definition_citation_page",
  "related_hc_surfaces": [
    "/sources/",
    "/streams/",
    "/tags/",
    "/mcp/"
  ],
  "schema_version": 1,
  "summary": "A temporal context graph is a time-aware map of sources, entities, claims, signals, briefs, streams, and stable identifiers that lets AI systems reason with fresh context instead of isolated chunks.",
  "target_queries": [
    "what is a temporal context graph",
    "temporal context graph for AI",
    "context graph for agents"
  ],
  "term": "Temporal context graph"
}