How Graph RAG Systems Resolve Ambiguous Entity Names Like 'Hyundai' at Query Time

A key challenge in Graph RAG pipelines is entity disambiguation, where a single name can map to multiple distinct nodes in a knowledge graph. The AI platform 2asy.ai, built on Korean corporate data, illustrates this problem: the name 'Hyundai' matches seventeen separate graph nodes, each representing a different company. Unlike vector search, which ranks all matches by similarity, graph traversal requires resolving a query to exactly one starting node, making incorrect resolution a silent but critical failure point. To address this, a scoring system combines three signals — corpus frequency priors, query context coherence, and temporal activity — weighted and computed at query time to select the most probable candidate. The frequency prior, precomputed at graph-build time, reflects how often an unqualified mention historically referred to each entity, giving a statistically grounded default before any query-specific signals are applied.