Triple

T2864082
Position Surface form Disambiguated ID Type / Status
Subject KEWR E63394 entity
Predicate hasTerminal P182 FINISHED
Object Terminal B E63489 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 B | Statement: [KEWR, hasTerminal, Terminal B]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal B
Context triple: [KEWR, hasTerminal, Terminal B]
  • A. Terminal B
    Terminal B is one of the passenger terminals at Sheremetyevo International Airport in Moscow, serving as a hub for domestic and selected international flights.
  • B. Terminal B
    Terminal B is a passenger terminal at San Jose International Airport serving commercial airline flights and travelers in San Jose, California.
  • C. Terminal B chosen
    Terminal B is one of the main passenger terminals at Newark Liberty International Airport, serving a mix of domestic and international flights with multiple concourses and airline operators.
  • D. Terminal B
    Terminal B is one of the passenger terminals at Dallas/Fort Worth International Airport, serving various domestic and regional flights with gates, check-in, and passenger amenities.
  • E. Terminal B
    Terminal B is one of the passenger terminals at John Wayne Airport in Orange County, California, serving commercial airline flights and related airport services.
  • 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_69ab4c42fb8c8190b36e161d47c03b81 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdfb853908190aa2fd492e9fa5e87 completed March 7, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0314baa408190b1b398dcfaa29c53 completed March 10, 2026, 2:57 p.m.
Created at: March 6, 2026, 10:02 p.m.