Triple

T1086576
Position Surface form Disambiguated ID Type / Status
Subject HAV E24064 entity
Predicate hasPassengerTerminal P1297 FINISHED
Object Terminal 2 unclear NED1 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 2 | Statement: [HAV, hasPassengerTerminal, Terminal 2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal 2
Context triple: [HAV, hasPassengerTerminal, Terminal 2]
  • A. Terminal 2
    Terminal 2 is one of the main passenger terminals at Manchester Airport, handling a large share of the airport’s international and domestic flights.
  • B. Terminal 2
    Terminal 2 is one of the main passenger terminals at Ontario International Airport in Southern California, serving domestic airline operations and traveler services.
  • C. Terminal 2
    Terminal 2 is one of the passenger terminals at Chicago O'Hare International Airport, serving various domestic and regional flights with multiple concourses and airline operations.
  • D. Terminal 2
    Terminal 2 is a secondary passenger terminal at Lisbon’s Humberto Delgado Airport, mainly serving low-cost and regional airlines.
  • E. Terminal 2
    Terminal 2 is a modern, sustainably designed passenger terminal at San Francisco International Airport known for its upgraded amenities, art installations, and improved traveler experience.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69a49404428c819092dcc9632f5f7b8b completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4b963161081908a523c8d63871652 completed March 1, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac8f6969a8819091383fb1172ceeee completed March 7, 2026, 8:49 p.m.
Created at: March 1, 2026, 7:42 p.m.