Triple

T6026586
Position Surface form Disambiguated ID Type / Status
Subject Cangzhou E134196 entity
Predicate seatOfGovernment P761 FINISHED
Object Yunhe District E626132 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: Yunhe District | Statement: [Cangzhou, seatOfGovernment, Yunhe District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yunhe District
Context triple: [Cangzhou, seatOfGovernment, Yunhe District]
  • A. Yunhe District chosen
    Yunhe District is an urban administrative district that serves as the central area and governmental seat of Cangzhou in Hebei Province, China.
  • B. Neihu District
    Neihu District is a suburban and technology-focused district in northeastern Taipei, Taiwan, known for its science parks, residential communities, and natural scenery.
  • C. Yingdong District
    Yingdong District is an urban administrative district of Fuyang City in Anhui Province, China.
  • D. Xialu District
    Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China.
  • E. Fengrun District
    Fengrun District is an administrative district under the jurisdiction of the prefecture-level city of Tangshan in Hebei Province, China.
  • 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_69c0087515148190a97475d412563865 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0560cdc308190b25ca8ecb42c4e4f completed March 22, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c74872582c81908c64a9bf925f67c6 completed March 28, 2026, 3:18 a.m.
Created at: March 22, 2026, 4:07 p.m.