Triple

T1084157
Position Surface form Disambiguated ID Type / Status
Subject Leonardo da Vinci–Fiumicino Airport E24012 entity
Predicate focusCityFor P164 FINISHED
Object Ryanair E4144 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: Ryanair | Statement: [Leonardo da Vinci–Fiumicino Airport, focusCityFor, Ryanair]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ryanair
Context triple: [Leonardo da Vinci–Fiumicino Airport, focusCityFor, Ryanair]
  • A. Ryanair chosen
    Ryanair is a major Irish low-cost airline known for its extensive network of short-haul flights across Europe.
  • B. Wizz Air
    Wizz Air is a Hungarian ultra-low-cost airline known for operating an extensive network of budget flights across Europe and surrounding regions.
  • C. Aer Lingus
    Aer Lingus is the flag carrier airline of Ireland, operating international flights primarily between Ireland, Europe, and North America.
  • D. Flybe
    Flybe was a British regional airline that operated short-haul flights across the UK and Europe before ceasing operations.
  • 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_69a49404428c819092dcc9632f5f7b8b completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4b9608bac819085196cc0f8fbb2e7 completed March 1, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac6f0c7d4c819082672393b0542af4 completed March 7, 2026, 6:31 p.m.
Created at: March 1, 2026, 7:42 p.m.