Entity Reconciliation in AI Search Systems As large language models increasingly power search interfaces, entity merging has emerged as a systemic issue. When two individuals share identical names, AI systems may blend achievements across separate entities. This phenomenon, known as cross-entity claim transfer, results from weak differentiation signals inside retrieval and generation pipelines. Entity reconciliation in AI search systems is the structured process of restoring accurate identity separation. The correction framework typically includes: • Schema-level identity reinforcement • Graph cluster separation • Retrieval-layer constraint tuning AI search misattribution correction processes begin with a full entity audit. This identifies where overlapping signals are being aggregated incorrectly. Identity boundary separation in large language models requires strengthening contextual markers such as profession, geography, institutional affiliation, and verified publ...
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