Triple

T14454836
Position Surface form Disambiguated ID Type / Status
Subject Maureen Stapleton E358430 entity
Predicate notableWork P4 FINISHED
Object Airport E857563 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: Airport | Statement: [Maureen Stapleton, notableWork, Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Airport
Context triple: [Maureen Stapleton, notableWork, Airport]
  • A. Airport
    "Airport" is the musical score composed by Alfred Newman for the 1970 disaster film of the same name, noted for its dramatic orchestral themes that underscore the movie’s tension and romance.
  • B. Airport chosen
    "Airport" is a 1970 American disaster-drama film, based on Arthur Hailey's novel, that helped launch the popular 1970s disaster movie genre.
  • C. Terminal Aérea
    Terminal Aérea is a Mexico City Metro station that serves the area around the Mexico City International Airport, providing convenient transit access for air travelers.
  • D. Airport Sector
    Airport Sector is a specialized unit of the Central Industrial Security Force (CISF) responsible for providing security and protection at airports across India.
  • E. Aeroport
    Aeroport is a Moscow Metro station on the Zamoskvoretskaya Line, named after the nearby Khodynka Aerodrome area.
  • 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_69d82794dfa081909b9134ad2e32244b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91a8bf088190abf5fd4f646b8c62 completed April 14, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd648f56608190b6d55c592c088575 completed May 8, 2026, 4:20 a.m.
Created at: April 10, 2026, 1:19 a.m.