想从LLM直接抽知识库？这篇论文给了一套实时消歧方案，产出了百万实体和3800多万三元组，跟以前维基系的做法很不一样。
GPTKB 2.0提出一种从LLM直接构建知识库的方法，无需依赖维基百科等外部资源。该方法在生成过程中实时消解实体、关系和类的歧义，兼顾规模与准确率。最终产出的知识库包含超过100万个消歧实体和3840万个三元组，是首个达到百万规模的LLM原生知识库。项目已开放，可在gptkb.org获取。
GPTKB 2.0: Direct Construction of Disambiguated Knowledge Bases from Large Language Models
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/.