Triple
T26283927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air France Flying Blue Explorer |
E661078
|
entity |
| Predicate | hasNoBenefit |
P66312
|
FINISHED |
| Object | no guaranteed priority check-in |
—
|
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: no guaranteed priority check-in | Statement: [Air France Flying Blue Explorer, hasNoBenefit, no guaranteed priority check-in]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoBenefit Context triple: [Air France Flying Blue Explorer, hasNoBenefit, no guaranteed priority check-in]
-
A.
hasNotableBenefaction
Indicates that an entity has provided a significant or noteworthy benefit, donation, or contribution to another entity or cause.
-
B.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
C.
benefitsNot
chosen
Indicates that one entity does not provide an advantage, help, or positive effect to another entity.
-
D.
hasDifferentBenefitsThan
Indicates that the benefits provided by one entity are not the same as those provided by another entity.
-
E.
hasBenefitType
Indicates that an entity is associated with a specific category or type of benefit it provides or receives.
- 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_69ee812bbd448190be4d7478b057990a |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: April 26, 2026, 10:02 p.m.