Triple

T22158252
Position Surface form Disambiguated ID Type / Status
Subject Hotien E547596 entity
Predicate transport P230 FINISHED
Object Hotan Airport 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: Hotan Airport | Statement: [Hotien, transport, Hotan Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hotan Airport
Context triple: [Hotien, transport, Hotan Airport]
  • A. Hotan Airport chosen
    Hotan Airport is a regional civil airport serving the city of Hotan in Xinjiang, China, providing both passenger and limited cargo air services.
  • B. Aksu Airport
    Aksu Airport is a regional civil airport serving the city of Aksu in Xinjiang, China, providing domestic air connections to other parts of the country.
  • C. Korla Airport
    Korla Airport is a regional civil airport serving the city of Korla in Xinjiang, China, providing both passenger and cargo air services.
  • D. Karamay Airport
    Karamay Airport is a regional civil aviation airport serving the city of Karamay in Xinjiang, China, providing passenger and cargo air transport connections to other parts of the country.
  • E. Kuqa Qiuci Airport
    Kuqa Qiuci Airport is a regional civil airport serving the city of Kuqa in Xinjiang, China, providing air transport connections for passengers and cargo in the surrounding area.
  • 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_69e11e3b52088190ad5df386d01eb2fb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f12a2aadb48190b4d739d1df9529db completed April 28, 2026, 9:44 p.m.
Created at: April 16, 2026, 8:33 p.m.