Triple

T7767700
Position Surface form Disambiguated ID Type / Status
Subject Gangseo District, Busan E178990 entity
Predicate contains P35 FINISHED
Object Gimhae International Airport E29657 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: Gimhae International Airport | Statement: [Gangseo District, Busan, contains, Gimhae International Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gimhae International Airport
Context triple: [Gangseo District, Busan, contains, Gimhae International Airport]
  • A. Gimhae International Airport chosen
    Gimhae International Airport is the main international airport serving the Busan metropolitan area in South Korea.
  • B. Gwangju Airport
    Gwangju Airport is a regional airport in Gwangju, South Korea, serving domestic flights and limited military operations.
  • C. Sacheon Airport
    Sacheon Airport is a regional airport in South Korea serving the city of Jinju and the surrounding Gyeongsangnam-do area with domestic flights.
  • D. Gunsan Airport
    Gunsan Airport is a regional airport in Gunsan, South Korea, serving both civilian flights and military operations.
  • E. Jeju International Airport
    Jeju International Airport is the main airport serving South Korea’s Jeju Island, handling extensive domestic traffic and a growing number of international flights for this major tourist destination.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c70435b7f88190a5e68e6ae701c58f completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8c7e4976c81909ff34dcdcae96999 completed March 29, 2026, 6:34 a.m.
Created at: March 27, 2026, 4:11 p.m.