Triple
T1363077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran Canaria Airport |
E29139
|
entity |
| Predicate | focusCityFor |
P164
|
FINISHED |
| Object | Vueling |
E28556
|
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: Vueling | Statement: [Gran Canaria Airport, focusCityFor, Vueling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vueling Context triple: [Gran Canaria Airport, focusCityFor, Vueling]
-
A.
Vueling
chosen
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.
-
B.
Lynx Air
Lynx Air is a Canadian ultra-low-cost airline that operates domestic and select international flights, primarily serving major hubs such as Toronto Pearson International Airport.
-
C.
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.
-
D.
Ibex Airlines
Ibex Airlines is a Japanese regional airline that operates domestic routes, often connecting smaller cities and regional airports within Japan.
-
E.
Ryanair
Ryanair is a major Irish low-cost airline known for its extensive network of short-haul flights across Europe.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b4ab3c8190ad692e32eee05976 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69addf2374ac8190ac2a2a753c056a3d |
completed | March 8, 2026, 8:42 p.m. |
Created at: March 1, 2026, 7:57 p.m.