Triple

T14064375
Position Surface form Disambiguated ID Type / Status
Subject Foshan E338426 entity
Predicate hasDistrict P459 FINISHED
Object Sanshui District E774995 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Sanshui District | Statement: [Foshan, hasDistrict, Sanshui District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sanshui District
Context triple: [Foshan, hasDistrict, Sanshui District]
  • A. Sanshui District chosen
    Sanshui District is an administrative district of Foshan City in Guangdong Province, China, known for its manufacturing base and location within the Pearl River Delta economic region.
  • B. Gaoming District
    Gaoming District is an administrative district of the city of Foshan in Guangdong Province, China, known for its mix of urban development and manufacturing-based economy.
  • C. Xinbei District
    Xinbei District is a major urban district and economic hub of Changzhou in Jiangsu Province, China, known for its modern development and industrial zones.
  • D. Nanhu District
    Nanhu District is an urban administrative district of Jiaxing in Zhejiang Province, China, known for encompassing the historic South Lake area.
  • E. Haizhu District
    Haizhu District is a central urban district of Guangzhou, China, known for its mix of residential areas, commercial centers, and cultural sites along the Pearl River.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5689c7f48190a47ca94eaa8a9ef9 completed April 14, 2026, 3 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff01d196d88190a8fa54468b2de1bb completed May 9, 2026, 9:43 a.m.
Created at: April 9, 2026, 10:21 p.m.