Organizational knowledge graph — ontological memory for business operations
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editorial(substrate): fix ontological-datagraph's unhedged auto-write claim + content-contract H1 violation (Track-B) — same real gap as nightly-datagraph-rebuild/knowledge-graph-grounded-apprenticeship: entities do not write straight to the graph automatically, a capture-then-promote human-approval checkpoint exists but ships off by default; cross-referenced rather than re-explaining; also fixed a pre-existing lint ERROR (duplicate body H1 duplicating the frontmatter title, content-contract section5.2) on both EN+ES; register-clean
@@ -10,15 +10,13 @@ index_group: small-language-model-stack short_description: "Organizational knowledge graph of people, companies, projects, and relationships — persistent semantic memory for answering business-state queries without re-reading sources." status: active bcsc_class: public-disclosure-safe last_edited: 2026-06-09 last_edited: 2026-08-22 editor: pointsav-engineering cites: [] references: [] paired_with: ontological-datagraph.es.md --- # The organizational knowledge graph — ontological memory for business operations An organizational knowledge graph stores what a business knows about itself: who its people, companies, and projects are; how they relate to one another; what decisions have been made and by whom; which policies govern which activities. This structured @@ -133,9 +131,8 @@ encodes that structure explicitly and makes it available at inference time. Entities enter the graph through an [[service-extraction|extraction pipeline]]. Documents, emails, meeting notes, and other prose sources arrive in a watched input directory. The extraction service reads each source, sends the text to the inference router for structured entity extraction using a grammar-constrained schema, and writes the resulting entities to the graph through the router's mutation endpoint. service reads each source and sends the text to the inference router for structured entity extraction using a grammar-constrained schema. The extraction quality depends on the inference tier. The local compact model (Tier A) extracts entities at lower confidence. The burst GPU node (Tier B) extracts @@ -143,7 +140,11 @@ at higher confidence using larger context windows and strict output constraints. Tier A extraction is useful for rapid coverage; Tier B extraction is used for the canonical organizational record. Every extraction is logged with a source reference and a confidence score. Entities Whether an extracted entity reaches the graph immediately or waits for a human-signed approval depends on an operator setting: a capture-then-promote checkpoint exists for exactly this case, but ships off by default — see [[nightly-datagraph-rebuild]] for the mechanism and its currently-open governance gap. Every extraction is logged with a source reference and a confidence score regardless of which path it takes. Entities extracted from authoritative sources (executed contracts, filed documents, official registrations) carry higher confidence than those extracted from informal correspondence.