Triple
T1691610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Europa |
E36560
|
entity |
| Predicate | frequentFlyerProgram |
P178
|
FINISHED |
| Object | Air Europa SUMA |
E36560
|
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: Air Europa SUMA | Statement: [Air Europa, frequentFlyerProgram, Air Europa SUMA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Air Europa SUMA Context triple: [Air Europa, frequentFlyerProgram, Air Europa SUMA]
-
A.
Air Europa
chosen
Air Europa is a Spanish airline that operates domestic and international flights, serving as one of Spain’s major carriers and a member of the SkyTeam alliance.
-
B.
Nightjet
Nightjet is a network of overnight long-distance passenger trains operated by Austrian Federal Railways (ÖBB) across several European countries.
-
C.
Smartavia
Smartavia is a Russian low-cost airline that operates domestic and regional flights, using Moscow Domodedovo International Airport as one of its main bases.
-
D.
Aena
Aena is the Spanish state-owned company that manages and operates the majority of airports in Spain and is one of the world’s largest airport operators by passenger traffic.
-
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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6298fa748190acabb9f1d42bd3f5 |
completed | March 6, 2026, 5:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad79947c908190b807205bd44c3254 |
completed | March 8, 2026, 1:28 p.m. |
Created at: March 4, 2026, 7:29 p.m.