Triple

T17344924
Position Surface form Disambiguated ID Type / Status
Subject RPVM E421658 entity
Predicate hasTerminal P182 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: [RPVM, hasTerminal, Terminal 2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terminal 2
Context triple: [RPVM, hasTerminal, Terminal 2]
  • A. 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.
  • B. Terminal 2
    Terminal 2 is the modern international passenger terminal at Nội Bài International Airport in Hanoi, Vietnam, serving most of the airport’s international flights.
  • C. Terminal 2
    Terminal 2 is the main, modern passenger terminal at Shanghai Hongqiao International Airport, handling the majority of the airport’s domestic and some international flights.
  • D. Terminal 2
    Terminal 2 is a secondary passenger terminal at Kota Kinabalu International Airport in Sabah, Malaysia, serving regional and low-cost airline operations.
  • E. Terminal 2
    Terminal 2 is one of the passenger terminals serving Iași International Airport in Romania, handling check-in, departures, and arrivals for commercial flights.
  • 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_69d889d520008190a26917a95bf1c2ea completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a27a350819086faf12e6bf9f0e2 completed April 19, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01954ecadc8190a6484ff0a207fe9b completed May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 5:44 a.m.