Triple
T13089589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WestJet Rewards |
E310424
|
entity |
| Predicate | coBrandedCardBenefit |
P2188
|
FINISHED |
| Object | accelerated earning on WestJet purchases |
—
|
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: accelerated earning on WestJet purchases | Statement: [WestJet Rewards, coBrandedCardBenefit, accelerated earning on WestJet purchases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coBrandedCardBenefit Context triple: [WestJet Rewards, coBrandedCardBenefit, accelerated earning on WestJet purchases]
-
A.
benefitAppliesTo
Indicates that a particular benefit is applicable to, or valid for, a specified entity or context.
-
B.
hasBenefit
chosen
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
C.
exclusiveBenefit
Indicates that a benefit is provided to one party or group in a way that excludes others from receiving the same advantage.
-
D.
interactionWithOtherBenefits
Indicates how this benefit relates to, depends on, or affects other benefits within the same system or context.
-
E.
cardEligibility
Indicates whether an entity qualifies for or is allowed to receive a particular card under specified criteria.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d98138a1d481908a139f2f67eb3472 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:03 p.m.