# Constructing Disambiguated Knowledge Bases from Large Language Models at Scale

arXiv: https://arxiv.org/abs/2608.03729 · HTML: https://mlsanity.com/paper/2608.03729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [EntGPT: Entity Linking with Generative Large Language Models](https://mlsanity.com/paper/2402.06738/) (2024)
- [Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5](https://mlsanity.com/paper/2510.07024/) (2025)
- [LLMpedia: A Transparent Framework to Materialize an LLM's Encyclopedic Knowledge at Scale](https://mlsanity.com/paper/2603.24080/) (2026)
- [Beyond Questions: Evaluating the Stated Knowledge of LLMs](https://mlsanity.com/paper/2605.26937/) (2026)

## Abstract (arXiv)

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/.