Triple

T22517784
Position Surface form Disambiguated ID Type / Status
Subject Jinyintan Station E556693 entity
Predicate locatedIn P40 FINISHED
Object Hubei NE NERFINISHED

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: Hubei | Statement: [Jinyintan Station, locatedIn, Hubei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hubei
Context triple: [Jinyintan Station, locatedIn, Hubei]
  • A. Hubei Province chosen
    Hubei Province is a landlocked region in central China known for its capital city Wuhan, major role in industry and transportation, and significant historical and cultural heritage.
  • B. Jiaozhi Province
    Jiaozhi Province was a Ming dynasty colonial administrative region established in northern Vietnam during the early 15th century.
  • C. Hebei
    Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
  • D. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • E. Zhili province
    Zhili province was a historically important administrative region in northern China, centered on present-day Hebei and Beijing, that played a key political and military role during the late Qing and early Republican eras.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e5657e881909f16ca58352c50da completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15e2f5c308190a0e6755a24340917 completed April 29, 2026, 1:26 a.m.
Created at: April 16, 2026, 8:50 p.m.