Triple

T17094441
Position Surface form Disambiguated ID Type / Status
Subject Gezhouba E414807 entity
Predicate locatedInProvince P40 FINISHED
Object Hubei E5772 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: Hubei | Statement: [Gezhouba, locatedInProvince, Hubei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hubei
Context triple: [Gezhouba, locatedInProvince, 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. Hebei
    Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
  • C. Kansu
    Kansu is a Turkish surname most notably associated with Şevket Aziz Kansu, a prominent Turkish academic and anthropologist.
  • D. 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.
  • E. Jiangsu
    Jiangsu is a populous and economically significant coastal province in eastern China, known for its rich history, dense urbanization, and major cities such as Nanjing and Suzhou.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbfc9158819081689d3d594a1908 completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0141422d6c819086dc98988c0851d9 completed May 11, 2026, 2:38 a.m.
Created at: April 10, 2026, 5:35 a.m.