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

2026-08-04

AI Summary

This work addresses the challenge that large language models (LLMs) lack explicit entity representations, which leads to entity duplication and ambiguity when constructing knowledge bases directly. To overcome this limitation, the authors propose a native disambiguation-based approach for knowledge base construction, leveraging LLM-driven knowledge extraction coupled with real-time disambiguation of entities, relations, and categories—without relying on external resources such as Wikimedia. The method achieves explicit internal normalization and yields a large-scale knowledge base comprising over one million disambiguated entities and 38.4 million triples. By simultaneously ensuring scalability, accuracy, and cost-efficiency, this approach represents a significant departure from conventional knowledge base construction paradigms.

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/.

Problem

• Knowledge Base Construction
• Entity Disambiguation
• Large Language Models
• Automated Knowledge Base Construction

Innovation

• disambiguated knowledge base
• large language models
• on-the-fly disambiguation
• entity canonicalization
• automated KB construction