Triple

T9823826
Position Surface form Disambiguated ID Type / Status
Subject Wutai County E238603 entity
Predicate locatedIn P40 FINISHED
Object Xinzhou City E339987 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: Xinzhou City | Statement: [Wutai County, locatedIn, Xinzhou City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xinzhou City
Context triple: [Wutai County, locatedIn, Xinzhou City]
  • A. Xinzhou chosen
    Xinzhou is a prefecture-level city in northern China known for its historical sites and location within Shanxi Province’s coal-rich and culturally significant region.
  • B. Jinzhong
    Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
  • C. Xinzhou District
    Xinzhou District is an outlying district of Wuhan, China, known for its ongoing urban development and integration into the city’s metro network.
  • D. Datong
    Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
  • E. Taiyuan
    Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
  • 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_69ca84e0dd1881909800765d1e21f735 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb316f8948190ada3738787a5cb6a completed April 2, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc810bac8190a5ff94c0717e7706 completed April 5, 2026, 2:44 a.m.
Created at: March 30, 2026, 8:31 p.m.