Triple

T6511156
Position Surface form Disambiguated ID Type / Status
Subject Hengshui E150136 entity
Predicate hasSubdivision P747 FINISHED
Object Taocheng District E607574 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: Taocheng District | Statement: [Hengshui, hasSubdivision, Taocheng District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taocheng District
Context triple: [Hengshui, hasSubdivision, Taocheng District]
  • A. Taocheng District chosen
    Taocheng District is the central urban district and administrative seat of Hengshui City in Hebei Province, China.
  • B. Liangxi District
    Liangxi District is a central urban district of Wuxi in Jiangsu Province, China, known as one of the city’s key commercial and administrative areas.
  • C. Fengnan District
    Fengnan District is an administrative district under the jurisdiction of the prefecture-level city of Tangshan in Hebei Province, China.
  • D. Yiling District
    Yiling District is an administrative district of Yichang in Hubei Province, China, known for its location along the Yangtze River and proximity to the Three Gorges region.
  • E. Taijiang District
    Taijiang District is a central urban district of Fuzhou in Fujian Province, China, known for its commercial activity and dense residential areas.
  • 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_69c687ef291081909d437f035eef1cda completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c69f3ad7d081909162f1a625fc52b1 completed March 27, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f7813a788190a25ace732cf4e3d0 completed March 27, 2026, 9:32 p.m.
Created at: March 27, 2026, 1:43 p.m.