@inproceedings{hu-etal-2026-constructing,
title = "Constructing Disambiguated Knowledge Bases from Large Language Models at Scale",
author = "Hu, Yujia and
Nguyen, Tuan-Phong and
Razniewski, Simon",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.199/",
pages = "4414--4432",
ISBN = "979-8-89176-439-2",
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.\par We propose DisamKB, a methodology for constructing disambiguated KBs directly from LLMs. DisamKB 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 DisamKB 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. DisamKB is anonymously available at https://gptkb.org/."
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<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.\par We propose DisamKB, a methodology for constructing disambiguated KBs directly from LLMs. DisamKB 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 DisamKB 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. DisamKB is anonymously available at https://gptkb.org/.</abstract>
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%0 Conference Proceedings
%T Constructing Disambiguated Knowledge Bases from Large Language Models at Scale
%A Hu, Yujia
%A Nguyen, Tuan-Phong
%A Razniewski, Simon
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F hu-etal-2026-constructing
%X 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.\par We propose DisamKB, a methodology for constructing disambiguated KBs directly from LLMs. DisamKB 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 DisamKB 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. DisamKB is anonymously available at https://gptkb.org/.
%U https://aclanthology.org/2026.aacl-long.199/
%P 4414-4432
Markdown (Informal)
[Constructing Disambiguated Knowledge Bases from Large Language Models at Scale](https://aclanthology.org/2026.aacl-long.199/) (Hu et al., AACL-IJCNLP 2026)
ACL
- Yujia Hu, Tuan-Phong Nguyen, and Simon Razniewski. 2026. Constructing Disambiguated Knowledge Bases from Large Language Models at Scale. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4414–4432, Hengqin, China. Association for Computational Linguistics.