Triple

T1120372
Position Surface form Disambiguated ID Type / Status
Subject Wuhan E11195 entity
Predicate hasAdministrativeDivision P747 FINISHED
Object Hongshan District E69519 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: Hongshan District | Statement: [Wuhan, hasAdministrativeDivision, Hongshan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hongshan District
Context triple: [Wuhan, hasAdministrativeDivision, Hongshan District]
  • A. Hongshan District chosen
    Hongshan District is an urban district of Wuhan in Hubei Province, China, known for its educational institutions, technology parks, and major transportation hubs.
  • B. Qingshan District
    Qingshan District is an urban district of Wuhan in Hubei Province, China, known for its heavy industry and riverside location along the Yangtze River.
  • C. Xicheng District
    Xicheng District is a central urban district of Beijing, China, known for its historic sites, government institutions, and cultural landmarks.
  • D. Nanshan District
    Nanshan District is a major urban district of Shenzhen, China, known as a key technology and innovation hub that hosts many leading tech companies and research institutions.
  • E. Pingshan District
    Pingshan District is an administrative district in the eastern part of Shenzhen, China, known for its emerging high-tech industries and rapid urban 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_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_69ac7f2dc92481909ee6d9d6d4257f1b completed March 7, 2026, 7:40 p.m.
Created at: March 1, 2026, 7:43 p.m.