Triple

T20796159
Position Surface form Disambiguated ID Type / Status
Subject Daegu E511913 entity
Predicate hasAirport P105 FINISHED
Object Daegu International 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: Daegu International Airport | Statement: [Daegu, hasAirport, Daegu International Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daegu International Airport
Context triple: [Daegu, hasAirport, Daegu International Airport]
  • A. Daegu International Airport chosen
    Daegu International Airport is a regional airport in Daegu, South Korea, serving both domestic and limited international flights.
  • B. Gwangju Airport
    Gwangju Airport is a regional airport in Gwangju, South Korea, serving domestic flights and limited military operations.
  • C. Pohang Airport
    Pohang Airport is a regional airport in Pohang, South Korea, serving both civilian flights and military operations.
  • D. Gimpo International Airport
    Gimpo International Airport is a major airport serving the Seoul metropolitan area, primarily handling domestic flights and regional international routes.
  • E. Asiana Airport
    Asiana Airport is an aviation-related company or facility associated with South Korea’s Kumho Asiana Group, likely involved in airport operation or services connected to Asiana Airlines.
  • 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_69e0b4cb83948190bd57bec21d78ed53 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2ad6f0481909e0bab7119f10f9c completed April 21, 2026, 12:19 a.m.
Created at: April 16, 2026, 12:39 p.m.