Triple

T9168450
Position Surface form Disambiguated ID Type / Status
Subject Yunmeng County E220022 entity
Predicate hasProvince P285 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: [Yunmeng County, hasProvince, Hubei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hubei
Context triple: [Yunmeng County, hasProvince, 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_69ca83e467108190abcae6a33b3d4dad completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccaadfb50881909b9127f92e4b3e21 completed April 1, 2026, 5:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69d178be71188190bb9bdb719dd4d12d completed April 4, 2026, 8:46 p.m.
Created at: March 30, 2026, 7:22 p.m.