A dataset of geographic entities and relationships from Song Dynasty texts on Lin'an

Overview GEPR-LINAN is a manually annotated dataset for named entity recognition (NER) and relation extraction (RE) from Classical Chinese historical texts. The dataset focuses on geographical entities and spatial relationships described in texts about Lin'an (临安, present-day Hangzhou, Zhejiang Province, China), the capital of the Southern Song Dynasty (1127–1279 CE). Lin'an was one of the largest and most prosperous cities in the medieval world. Its spatial organisation — encompassing palaces, government offices, Buddhist and Taoist establishments, markets, gardens, waterways, and street networks — is documented in exceptional detail across a range of surviving local gazetteers and miscellaneous notes. These texts provide a foundation for computational reconstruction of the city's historical geography but pose significant challenges for NLP systems due to archaic vocabulary, elliptical syntax, and dense spatial expressions. The dataset provides annotations for 24 geographical entity types and 34 spatial and semantic relationship types, covering 4,920 sentences drawn from 18 text units drawn from 15 in-domain (IND) classical Chinese source works and one out-of-distribution (OOD) source. Metric Value Entity types 24 Relation types 34 Total sentences 4,920 Total entities 25,397 Total relations 17,881 Source texts (IND) 18 text units / 15 source works / 88 volumes Source texts (OOD) 1 work / 2 volumes IAA — Entity F1 86.38% IAA — Relation F1 77.82%