Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Evidence artifactsite-context.md
- 03Evidence artifactai-manifest.json
- 04Evidence artifactai-governance.json
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
site-context.md
/site-context.md
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
ai-manifest.json
/ai-manifest.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
ai-governance.json
/.well-known/ai-governance.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
Complementary probative surfaces (2)
These artifacts extend the main chain. They help qualify an audit, an evidence level, a citation, or a version trajectory.
entity-graph.jsonld
/entity-graph.jsonld
Published surface that contributes to making an evidence chain more reconstructible.
llms.txt
/llms.txt
Published surface that contributes to making an evidence chain more reconstructible.
Entity graph
This page is the canonical definition of entity graph within the canon, corpus, and machine readability layer of interpretive governance.
An entity graph is a structured representation of entities, identities, relations, roles, authoritative links, and conceptual associations used to reduce ambiguity in machine interpretation.
Short definition
An entity graph is a structured representation of entities, identities, relations, roles, authoritative links, and conceptual associations used to reduce ambiguity in machine interpretation.
Why it matters
It anchors the site’s person, concepts, artifacts, pages, and relationships so that machines do not reconstruct identity from fragments, aliases, proximity, or third-party summaries alone.
In AI search, retrieval-augmented generation, autonomous browsing, and agentic reading, a corpus is not interpreted only by its visible prose. It is interpreted through routes, files, metadata, exclusions, entity relations, sitemap placement, and internal links. Entity graph names one part of that documentary control layer.
The strategic function is therefore not cosmetic. The concept helps prevent systems from flattening doctrine, service language, proof artifacts, and observations into the same authority level. It also gives search engines a clearer canonical page to associate with the term rather than forcing them to choose between a hub, a category, a blog article, and a machine artifact.
What it is not
It is not a knowledge graph guarantee, not a claim that external search systems will adopt every relation, and not an unrestricted expansion of associations.
This distinction matters because machine-readable governance can create false confidence. A structured file, a definition page, or a graph relation should never be treated as proof that external systems comply with the intended reading. It only makes the intended reading more explicit, testable, and auditable.
Common failure modes
- entity relations are implicit rather than declared;
- concepts are linked to the author without role boundaries;
- external systems confuse the person, doctrine, services, and artifacts;
- graph data implies services or capabilities that are globally excluded;
These failures are typical when the human corpus and the machine-first corpus evolve separately. They increase interpretive risk because models can still produce coherent answers while violating the source hierarchy or ignoring exclusions.
Governance implication
The entity graph should express identity and concept relations while preserving exclusions, authority boundaries, and non-inference rules. It should clarify relations rather than inflate them.
For SERP ownership, the same principle applies: the canonical page should receive descriptive links, appear in the definitions registry, be discoverable from the glossary, and be reinforced by machine-first artifacts without competing against them.
Related canonical definitions
- AI manifest
- AI governance JSON
- Machine readability
- Documentary architecture
- Global exclusions
- Semantic Integrity
- Ai Disambiguation