Triple

T4247077
Position Surface form Disambiguated ID Type / Status
Subject Wizz Air E95554 entity
Predicate foundedAs P364 FINISHED
Object Wizz Air Hungary Ltd. 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 Hungary Ltd. | Statement: [Wizz Air, foundedAs, Wizz Air Hungary Ltd.]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wizz Air Hungary Ltd.
Context triple: [Wizz Air, foundedAs, Wizz Air Hungary Ltd.]
  • 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. Malev Hungarian Airlines
    Malev Hungarian Airlines was the former national flag carrier of Hungary, operating scheduled passenger flights across Europe and beyond until its closure in 2012.
  • C. Budapest Airport Zrt.
    Budapest Airport Zrt. is the company responsible for managing and operating Hungary’s main international gateway, Budapest Ferenc Liszt International Airport.
  • 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. 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.
  • 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_69b3453d91548190b4d4ef8fe52aa2ac completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e9b64ac81908dc44eaae6829b50 completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a87c033881908e0cf9fdfecaf36a completed March 14, 2026, 6:27 p.m.
Created at: March 12, 2026, 11:05 p.m.