GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base

2026-08-07

AI Summary

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.

Abstract

We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM). GPTKB 2.0 contains 38.4M triples over 1.6M canonical entities, together with 207.6K consolidated relations and 66K consolidated classes. Unlike prior LLM-derived knowledge bases that largely identify entities by surface strings, GPTKB 2.0 performs context-guided disambiguation during recursive KB construction, separating homonyms and merging synonymous mentions as facts are elicited. The demo makes this process inspectable: users can browse entities, follow links across the KB, and audit the provenance of individual facts, including surface forms, candidate matches, source triples, and disambiguation decisions. The interface further supports structured SPARQL queries, natural-language questions translated to SPARQL, and entity linking from user-provided text to canonical GPTKB 2.0 entries. GPTKB 2.0 is available at https://gptkb.org/, with the full KB downloadable for offline use.

Problem

• knowledge base
• entity disambiguation
• large language model
• canonicalization
• homonym separation

Innovation

• disambiguated knowledge base
• context-guided disambiguation
• LLM-derived KB
• entity linking
• provenance auditing