LLMs keep getting more fluent-but can you actually verify what they say？ Structured KBs like Wikidata lack text grounding. Annotation-based datasets like FEVER are too small and monolingual. Synthetic expansion just produces hallucinations at scale. The trilemma between authenticity， scale， and structure has gone unsolved. ❓ Today， we dive into FactNet-a landmark contribution by @TsinghuaNLP （OpenBMB member） alongside researchers from TU Munich， Modelbest Inc.， and Minzu University of China. FactNet constructs a billion-scale， open-source multilingual knowledge graph that unifies structured Wikidata assertions with auditable， byte-level evidence pointers from 316 native Wikipedia editions. 🤗 Paper： https://huggingface.co/papers/2602.03417 📄 arXiv： https://arxiv.org/abs/2602.03417 💻 Code & Data： https://github.com/yl-shen/factnet