Triple

T1970119
Position Surface form Disambiguated ID Type / Status
Subject Suizhou E42779 entity
Predicate isSeatOf P62 FINISHED
Object Zengdu District E219425 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: Zengdu District | Statement: [Suizhou, isSeatOf, Zengdu District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zengdu District
Context triple: [Suizhou, isSeatOf, Zengdu District]
  • A. Zengdu District chosen
    Zengdu District is an urban administrative district of Suizhou City in Hubei Province, China, serving as one of its key political and economic centers.
  • B. Hechuan District
    Hechuan District is an urban district of the Chongqing municipality in southwestern China, known for its historical significance and location at the confluence of three rivers.
  • C. Jiulongpo District
    Jiulongpo District is an urban administrative district of Chongqing, China, known for its industrial base and growing residential and commercial areas.
  • D. Yuhua District
    Yuhua District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
  • E. Tianxin District
    Tianxin District is a central urban district of Changsha, the capital city of Hunan Province in China, known for its historical sites and commercial 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_69a88711151c8190940b2572095059d7 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb3d17274819084cd352a3d2a8151 completed March 7, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5176d2f08190b3ebc53ea1def9be completed March 9, 2026, 4:49 a.m.
Created at: March 4, 2026, 7:36 p.m.