Triple

T325194
Position Surface form Disambiguated ID Type / Status
Subject KLAX E6499 entity
Predicate hasTerminal P182 FINISHED
Object Terminal 4 E15362 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: Terminal 4 | Statement: [KLAX, hasTerminal, Terminal 4]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal 4
Context triple: [KLAX, hasTerminal, Terminal 4]
  • A. Terminal 4
    Terminal 4 is a major international passenger terminal at John F. Kennedy International Airport in New York City, serving numerous global airlines and long-haul routes.
  • B. Terminal 4 chosen
    Terminal 4 is one of the main passenger terminals at Los Angeles International Airport, primarily serving American Airlines and several domestic and international flights.
  • C. Terminal 4
    Terminal 4 is the largest and primary passenger terminal at Phoenix Sky Harbor International Airport, serving most of the airport’s domestic and international flights.
  • D. Terminal 5
    Terminal 5 is one of the passenger terminals at José Martí International Airport in Havana, Cuba, serving specific airlines and routes.
  • E. Terminal 5
    Terminal 5 is a major passenger terminal at John F. Kennedy International Airport in New York City, known for serving as the primary hub for JetBlue Airways.
  • 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_69a2e7933d6c8190bb2592ad13286ef2 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea959f9c819084602b8a1b5e66dd completed Feb. 28, 2026, 1:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3d7e47f1c8190a4152dcb5c662514 completed March 1, 2026, 6:08 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.