Triple

T2422583
Position Surface form Disambiguated ID Type / Status
Subject Shenzhen Airlines E53450 entity
Predicate focusCity P164 FINISHED
Object Haikou E197372 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: Haikou | Statement: [Shenzhen Airlines, focusCity, Haikou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haikou
Context triple: [Shenzhen Airlines, focusCity, Haikou]
  • A. Haikou chosen
    Haikou is the capital and largest city of China’s Hainan Province, known as a key port, commercial hub, and tropical coastal destination.
  • B. Sanya
    Sanya is a major resort city on the southern coast of China’s Hainan Island, known for its tropical climate and popular beach tourism.
  • C. Wanning
    Wanning is a county-level coastal city in southeastern Hainan, China, known for its tropical climate, beaches, and surf-friendly bays.
  • D. Zhuhai
    Zhuhai is a coastal city in Guangdong Province, China, known for its proximity to Macau, its role in the Pearl River Delta economic zone, and its reputation as a popular tourist destination.
  • E. Beihai
    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.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc972934481909e05bd6f31162f9d completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf5b1bb8819095920d702c180d3f completed March 9, 2026, 12:38 p.m.
Created at: March 6, 2026, 9:42 p.m.