Triple

T83567
Position Surface form Disambiguated ID Type / Status
Subject Wuhan E1680 entity
Predicate capitalOf P204 FINISHED
Object Hubei Province 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 Province | Statement: [Wuhan, capitalOf, Hubei Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hubei Province
Context triple: [Wuhan, capitalOf, Hubei Province]
  • 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. Wuhan
    Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
  • C. Wuling
    Wuling is a Chinese automotive marque known for producing affordable compact cars and microvans, marketed through a joint venture involving General Motors.
  • D. Guangzhou
    Guangzhou is a major port city in southern China and the capital of Guangdong Province, known as a key commercial and manufacturing hub in the Pearl River Delta.
  • E. Wuhan University
    Wuhan University is a major comprehensive research university in Wuhan, China, renowned for its strong academic programs and picturesque campus.
  • 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_69a24c8150408190910a693eb51c1f71 completed Feb. 28, 2026, 2:01 a.m.
NER Named-entity recognition batch_69a24f4ccb5081908decac81f4af01bf completed Feb. 28, 2026, 2:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69a25abdf36c819087c4be57bd8ce8c5 completed Feb. 28, 2026, 3:02 a.m.
Created at: Feb. 28, 2026, 2:06 a.m.