Triple

T7639149
Position Surface form Disambiguated ID Type / Status
Subject Katowice Airport E172954 entity
Predicate focusCityFor P164 FINISHED
Object Wizz Air E95554 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: Wizz Air | Statement: [Katowice Airport, focusCityFor, Wizz Air]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wizz Air
Context triple: [Katowice Airport, focusCityFor, Wizz Air]
  • A. Wizz Air chosen
    Wizz Air is a Hungarian ultra-low-cost airline known for operating an extensive network of budget flights across Europe and surrounding regions.
  • B. Ryanair
    Ryanair is a major Irish low-cost airline known for its extensive network of short-haul flights across Europe.
  • C. Vueling
    Vueling is a Spanish low-cost airline that operates extensive domestic and European routes, particularly around major hubs such as Barcelona and other key cities.
  • D. Crossair
    Crossair was a former Swiss regional airline that served as the main predecessor to Swiss International Air Lines after the collapse of Swissair.
  • E. Eurowings
    Eurowings is a German low-cost airline and Lufthansa subsidiary that operates short- and long-haul flights across Europe and selected international destinations.
  • 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_69c69952849881908fdcea7a93bfc307 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6facc4b5481908697e662b0991e3f completed March 27, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69c870cd11648190b9aeedadb7bc1981 completed March 29, 2026, 12:22 a.m.
Created at: March 27, 2026, 3:57 p.m.