Triple
T764929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nice Côte d’Azur Airport |
E16153
|
entity |
| Predicate | hasHubAirline |
P423
|
FINISHED |
| Object | easyJet |
E6907
|
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: easyJet | Statement: [Nice Côte d’Azur Airport, hasHubAirline, easyJet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: easyJet Context triple: [Nice Côte d’Azur Airport, hasHubAirline, easyJet]
-
A.
easyJet
chosen
easyJet is a major British low-cost airline operating extensive domestic and European routes.
-
B.
Ryanair
Ryanair is a major Irish low-cost airline known for its extensive network of short-haul flights across Europe.
-
C.
TUI Airways
TUI Airways is a British charter and scheduled airline that primarily serves leisure destinations across Europe and worldwide as part of the TUI Group.
-
D.
Jet2.com
Jet2.com is a British low-cost leisure airline that operates scheduled and charter flights across Europe from multiple UK bases.
-
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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a69dfeb08190b54a476cfa66e6d6 |
completed | March 1, 2026, 8:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a67efa8dd481909097c551bf3a61dc |
completed | March 3, 2026, 6:26 a.m. |
Created at: March 1, 2026, 7:37 p.m.