Entity disambiguation
This expertise axis addresses the mechanisms aimed at stabilizing entity identification (persons, brands, organizations, concepts) in order to reduce confusions, semantic collisions, and erroneous attributions.
Disambiguation does not aim to impose a narrative. It aims to make an entity harder to confuse, easier to cross-reference, and less vulnerable to default inferences.
This work relies on the canonical definition AI disambiguation and is attached to the mechanism of interpretive governance.
Problem
When multiple entities share similar attributes (name, sector, vocabulary, geography, history), inference systems tend to merge signals, project plausible associations, or attribute elements to the wrong entity.
The problem is not the absence of content. The problem is the absence of stable perimeters and cross-referenceable relations between the surfaces that describe the entity.
Typical consequences
- Persistent homonymy despite a coherent site.
- Erroneous attribution of projects, roles, publications, or capabilities.
- Abusive fusion between a brand, a product, an organization, and a person.
- Divergent descriptions depending on the engines and assistants consulted.
- Progressive identity dilution through association shift.
Conceptual levers
- Canonical entity: entity page, identifiers, stable properties, limits.
- Canonical relations: real and directional identity links, coherent references.
- Bounding and exclusions: explicit non-equivalences, inference perimeters.
- Controlled redundancy: coherent repetition of the same facts across compatible surfaces.
- Semantic graph: articulation of entities and their relations, without extrapolation.
Canonical references
Back to the map: Expertise.
