Triple

T11050595
Position Surface form Disambiguated ID Type / Status
Subject Terminal 2 (Ninoy Aquino International Airport) E261234 entity
Predicate servesAirline P12356 FINISHED
Object PAL Express E222068 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: PAL Express | Statement: [Terminal 2 (Ninoy Aquino International Airport), servesAirline, PAL Express]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PAL Express
Context triple: [Terminal 2 (Ninoy Aquino International Airport), servesAirline, PAL Express]
  • A. PAL Express chosen
    PAL Express is a Philippine low-cost regional airline brand operating domestic and select international flights on behalf of Philippine Airlines.
  • B. UP Express
    UP Express is a dedicated airport rail link in Toronto that provides fast, frequent train service between Union Station downtown and Toronto Pearson International Airport.
  • C. NAS EXPRESS
    NAS EXPRESS is the airline callsign used by Flynas, a Saudi Arabian low-cost carrier.
  • D. LAN Express
    LAN Express was a Chilean regional airline operating domestic and short-haul routes as a subsidiary brand of LAN Airlines (now part of LATAM Airlines Group).
  • E. Universal Express
    Universal Express is a paid line-skipping system used at Universal theme parks to reduce wait times for popular attractions.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d798698bd88190aa97afd37f55e19f completed April 9, 2026, 12:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3aa146b148190a87205e542cc718f completed April 18, 2026, 3:58 p.m.
Created at: April 8, 2026, 9:26 p.m.