Triple

T6086693
Position Surface form Disambiguated ID Type / Status
Subject Wuhan Metro Line 4 E135655 entity
Predicate connects P390 FINISHED
Object Caidian District E620869 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: Caidian District | Statement: [Wuhan Metro Line 4, connects, Caidian District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Caidian District
Context triple: [Wuhan Metro Line 4, connects, Caidian District]
  • A. Caidian District chosen
    Caidian District is an administrative district in the western part of Wuhan, China, known for its rapid urban development and integration into the city’s metro network.
  • B. Dawan District
    Dawan District is an administrative district in Klungkung Regency on the island of Bali, Indonesia.
  • C. Chenghua District
    Chenghua District is an urban district of Chengdu, China, best known internationally as the home of the Chengdu Research Base of Giant Panda Breeding.
  • D. Dongbao District
    Dongbao District is an urban administrative district under the jurisdiction of Jingmen City in Hubei Province, China, serving as the city's central area.
  • E. Xiaodian District
    Xiaodian District is an urban district of Taiyuan, the capital city of Shanxi Province in northern China, known for its residential areas and growing commercial development.
  • 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_69c0087bcc788190b20f093d3a6c60ec completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0578a8b8081908490e447ae3419f9 completed March 22, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c77510f80c81908fe7784bdaa640c5 completed March 28, 2026, 6:28 a.m.
Created at: March 22, 2026, 4:12 p.m.