Entity Resolution

Entity Resolution is the process by which an AI system determines that multiple references across the web — a company name on LinkedIn, a brand mention in Forbes, a schema definition on a corporate site, a Wikidata entry — all refer to the same real-world entity. When entity resolution works, the AI aggregates authority. When it fails, your brand's signals scatter across fragmented identities, and the machine treats you as multiple weak entities instead of one strong one.

Why Entity Resolution Matters

Every AI-generated answer requires the model to decide which entities to include. That decision depends on the AI's confidence that it has correctly identified and understood the entity. Low-confidence entities get omitted. Ambiguous entities get confused with competitors or generic terms. Only entities the system can cleanly resolve earn citations.

This is especially critical for brands with common names, acronyms shared with other organizations, or names that overlap with dictionary words. If an AI engine can't confidently distinguish your brand from background noise, it defaults to safer, better-resolved competitors.

How Entity Resolution Works in AI Systems

AI engines resolve entities through convergent signals:

• Knowledge graph entries. Google Knowledge Graph, Wikidata, and Crunchbase provide canonical entity definitions. A Knowledge Panel is strong evidence that Google has resolved your entity.
• Schema.org sameAs links. Explicit connections between your website and authoritative profiles (LinkedIn, Crunchbase, Wikipedia) tell the AI that these references are the same entity.
• Cross-source consistency. When your brand name, description, category, and key attributes match across multiple independent sources, the AI's confidence in resolution increases. Inconsistency — different names, conflicting descriptions, mismatched categories — degrades resolution.
• Earned media mentions. Third-party publications that reference your brand with consistent naming and context provide corroborating evidence for entity identity.

Entity Resolution Failures

Common failure modes include: brands with generic names that collide with dictionary terms; companies that rebranded without updating all digital footprints; organizations with inconsistent naming across platforms (abbreviations on one, full names on another); and brands that lack any knowledge graph presence. Each failure mode causes the AI to either split your authority across multiple entities or merge you with something you're not.

Entity optimization is the strategic discipline of preventing these failures. Entity signals are the individual data points that feed the resolution process. Clean resolution is the prerequisite — without it, no amount of citation architecture can compensate.