Triple

T12947289
Position Surface form Disambiguated ID Type / Status
Subject Yinhai District E309802 entity
Predicate locatedIn P40 FINISHED
Object Beihai City E56871 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: Beihai City | Statement: [Yinhai District, locatedIn, Beihai City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beihai City
Context triple: [Yinhai District, locatedIn, Beihai City]
  • A. Beihai chosen
    Beihai is a coastal city in China's Guangxi Zhuang Autonomous Region, known for its beaches, maritime trade, and the scenic Silver Beach tourist area.
  • B. Qinzhou
    Qinzhou is a coastal prefecture-level city in southern China known for its port, maritime industries, and location along the Gulf of Tonkin in the Guangxi Zhuang Autonomous Region.
  • C. Wanning
    Wanning is a county-level coastal city in southeastern Hainan, China, known for its tropical climate, beaches, and surf-friendly bays.
  • D. Beihai Haicheng District
    Beihai Haicheng District is a central urban district of Beihai City in Guangxi, China, known as an administrative and commercial hub along the Gulf of Tonkin.
  • E. Laohekou City
    Laohekou City is a county-level city in northwestern Hubei Province, China, known as a regional transport and commercial hub under the administration of Xiangyang.
  • 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_69d7bdfb57a88190836b743e2825feca completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97e1c67b8819094e5243267f93ce2 completed April 10, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7265af6cc81908837c80797a8e704 completed May 3, 2026, 10:41 a.m.
Created at: April 9, 2026, 5:43 p.m.