Triple

T6843631
Position Surface form Disambiguated ID Type / Status
Subject Huangshan (city) E157836 entity
Predicate contains P35 FINISHED
Object Huizhou District E672168 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: Huizhou District | Statement: [Huangshan (city), contains, Huizhou District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Huizhou District
Context triple: [Huangshan (city), contains, Huizhou District]
  • A. Huizhou District chosen
    Huizhou District is an administrative district of Huangshan City in Anhui Province, China, known for its historic Huizhou culture and traditional architecture.
  • B. Xiangzhou District
    Xiangzhou District is the central urban district of Zhuhai in Guangdong Province, China, known for its government, commercial, and coastal areas facing Macau.
  • C. 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.
  • D. 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.
  • E. Pingshan District
    Pingshan District is an administrative district in the eastern part of Shenzhen, China, known for its emerging high-tech industries and rapid urban development.
  • 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_69c6882ed4c081909dc465a7cf8838be completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d6b7179481909e3482fef47b2719 completed March 27, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69c861218ac081909798edadae16162f completed March 28, 2026, 11:15 p.m.
Created at: March 27, 2026, 2:19 p.m.