Triple
T1363076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran Canaria Airport |
E29139
|
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: [Gran Canaria Airport, focusCityFor, Ryanair]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ryanair Context triple: [Gran Canaria 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_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_69add1a428b88190a79a669ca6b73671 |
completed | March 8, 2026, 7:44 p.m. |
Created at: March 1, 2026, 7:57 p.m.