Triple

T15639439
Position Surface form Disambiguated ID Type / Status
Subject Wuhan suburban districts E376028 entity
Predicate hasMember P10 FINISHED
Object Caidian District NE NERFINISHED

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 suburban districts, hasMember, Caidian District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Caidian District
Context triple: [Wuhan suburban districts, hasMember, 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. Huadu District
    Huadu District is a suburban district in the northern part of Guangzhou, China, known for its growing urban development and transportation links, including metro and rail connections.
  • C. Wensheng District
    Wensheng District is an urban district within the prefecture-level city of Liaoyang in Liaoning Province, northeastern China.
  • D. Dawan District
    Dawan District is an administrative district in Klungkung Regency on the island of Bali, Indonesia.
  • E. Yunxi District
    Yunxi District is an urban administrative district of Yueyang City in Hunan Province, China, known for its location along the Yangtze River and Dongting Lake region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d85cd035a48190b73d5579ab73969a completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ed06b388190bfebb77fe70e7df1 completed April 16, 2026, 2:52 a.m.
Created at: April 10, 2026, 4:14 a.m.