GPTKB 2.0: Direct Construction of Disambiguated Knowledge Bases from Large Language Models

Researchers introduced GPTKB 2.0, a methodology for constructing disambiguated knowledge bases directly from large language models, achieving a materialized KB with over 1 million disambiguated entities and 38.4 million triples. The system, available at https://gptkb.org/, incorporates on-the-fly disambiguation of entities, relations, and classes, marking the first million-scale LLM-native KB with explicit internal canonicalization.

arXiv:2608.03729v1 Announce Type: new Abstract: Automated Knowledge Base Construction AKBC is a core NLP task, and recent work proposes generating knowledge bases directly from large language models LLMs , treating the model itself as the knowledge source. However, LLMs natively possess no representation of entities, leading to duplicate entries as well as conflations. We propose GPTKB 2.0, a methodology for constructing disambiguated KBs directly from LLMs. GPTKB 2.0 incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy. We analyze the central design decisions and characterize the trade-offs between accuracy, scale, and cost. We execute GPTKB 2.0 at scale, obtaining a materialized KB containing over 1M disambiguated entities and 38.4M triples. This represents the first million-scale LLM-native KB with explicit internal canonicalization of entities, relations, and classes, a significant departure from prior Wikimedia-centric works. GPTKB 2.0 is available at https://gptkb.org/.