Triple

T12707837
Position Surface form Disambiguated ID Type / Status
Subject Yingkou E303634 entity
Predicate administers P123 FINISHED
Object Xishi District E1037455 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: Xishi District | Statement: [Yingkou, administers, Xishi District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xishi District
Context triple: [Yingkou, administers, Xishi District]
  • A. Shuangxi District
    Shuangxi District is a rural, mountainous district in eastern New Taipei City, Taiwan, known for its rivers, old streets, and natural scenery.
  • B. Jianye District
    Jianye District is an urban district of Nanjing, China, known for its historical significance and major memorial sites related to the Nanjing Massacre.
  • C. Tianning District
    Tianning District is an urban administrative district of Changzhou in Jiangsu Province, China, known for its historic temples and commercial centers.
  • D. Zhanqian District chosen
    Zhanqian District is an urban administrative district under the jurisdiction of Yingkou City in Liaoning Province, China.
  • E. Binhu District
    Binhu District is an urban district of Wuxi in Jiangsu Province, China, known for its lakeside scenery along Taihu Lake and its role as a key residential and economic area of the city.
  • 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_69d7bdef90d48190b46b88270e780946 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9620663e881908d367170ed6d2c81 completed April 10, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7304f62288190aa7788fc6fb04254 completed May 3, 2026, 11:23 a.m.
Created at: April 9, 2026, 5:23 p.m.