Triple

T11058554
Position Surface form Disambiguated ID Type / Status
Subject ANA Group E261443 entity
Predicate subsidiary P258 FINISHED
Object ANA Wings E216824 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: ANA Wings | Statement: [ANA Group, subsidiary, ANA Wings]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ANA Wings
Context triple: [ANA Group, subsidiary, ANA Wings]
  • A. ANA Wings chosen
    ANA Wings is a Japanese regional airline operating domestic feeder and short-haul services on behalf of All Nippon Airways.
  • B. Wing
    Wing is an Alphabet Inc. subsidiary focused on developing and operating drone-based delivery services and related logistics technologies.
  • C. Wing
    Wing is an experimental mobile operating system and user interface project developed by X (formerly Google X) to explore new paradigms in smartphone interaction and design.
  • D. Albawings
    Albawings is an Albanian low-cost airline based in Tirana that operates short-haul passenger flights, primarily connecting Albania with destinations in Europe.
  • E. WINGO
    WINGO is the radio callsign used by Wingo, a low-cost Colombian airline operating domestic and international flights in Latin America.
  • 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_69d798a2efa48190b290f43dfe836501 completed April 9, 2026, 12:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3c87ab0308190a6a6ada1708f0ec2 completed April 18, 2026, 6:07 p.m.
Created at: April 8, 2026, 9:26 p.m.