GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base
Traditional knowledge bases derived from large language models often suffer from ambiguity and unreliability due to their reliance on surface-level string matching, which fails to disambiguate homonyms or consolidate synonymous expressions. This work proposes a recursive knowledge extraction framework that incorporates a context-guided entity disambiguation mechanism during construction, enabling, for the first time in LLM-derived knowledge bases, effective synonym consolidation and homonym separation. The resulting knowledge base comprises 38.4 million triples, 1.6 million canonicalized entities, 207,600 integrated relations, and 66,000 unified categories. It further integrates entity linking, relation and category clustering, a SPARQL query engine, and a natural language-to-SPARQL translation module, offering an auditable, browsable, and queryable interactive web platform alongside full public data release.