Triple
T7312553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air France Flying Blue Gold |
E168128
|
entity |
| Predicate | loungeAccessOn |
P28414
|
FINISHED |
| Object | Air France flights |
—
|
LITERAL 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 France flights | Statement: [Air France Flying Blue Gold, loungeAccessOn, Air France flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loungeAccessOn Context triple: [Air France Flying Blue Gold, loungeAccessOn, Air France flights]
-
A.
hasLoungeType
Indicates that an entity is associated with, or classified by, a particular type or category of lounge.
-
B.
accessibleOn
chosen
Indicates that one entity can be reached, used, or obtained through another entity (such as a platform, device, or medium).
-
C.
hasAmenityAccessTo
Indicates that an entity has the right or ability to use or benefit from a specified amenity or facility.
-
D.
hasCustomerLounge
Indicates that an entity provides or includes a designated lounge area for customers to use.
-
E.
hasLoungeBrand
Indicates that an entity is associated with, or operates under, a particular lounge brand.
- F. None of above.
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_69c6888d8e3c81909db79714903baf31 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ec02319c819096d25e3683943886 |
completed | March 27, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69c6e7705f4881909793071dee50c557 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3:02 p.m.