Triple

T1120432
Position Surface form Disambiguated ID Type / Status
Subject Wuhan Metro E11197 entity
Predicate serves P98 FINISHED
Object Wuchang E1680 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: Wuchang | Statement: [Wuhan Metro, serves, Wuchang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wuchang
Context triple: [Wuhan Metro, serves, Wuchang]
  • A. Wuhan chosen
    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.
  • B. Huangshi
    Huangshi is an industrial city in eastern Hubei Province, China, known for its steel production and location along the Yangtze River.
  • C. Tongling
    Tongling is a prefecture-level city in eastern China known for its rich copper resources and mining industry.
  • D. Yichang
    Yichang is a key city in western Hubei, China, best known as the gateway to the Three Gorges region and the nearby Three Gorges Dam on the Yangtze River.
  • E. Xiangyang
    Xiangyang is a historic prefecture-level city in northern Hubei Province, China, known for its strategic location on the Han River and well-preserved ancient city walls.
  • 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_69a493252a648190ac48f8742474a5e8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4bbbe58588190a5ef6346e269d5f3 completed March 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae0a966a6c81909d1d21c489340134 completed March 8, 2026, 11:47 p.m.
Created at: March 1, 2026, 7:43 p.m.