Triple
T10399905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premier Platinum |
E245117
|
entity |
| Predicate | eliteMileageBonus |
P57184
|
FINISHED |
| Object | 75% bonus award miles on eligible 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: 75% bonus award miles on eligible flights | Statement: [Premier Platinum, eliteMileageBonus, 75% bonus award miles on eligible flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eliteMileageBonus Context triple: [Premier Platinum, eliteMileageBonus, 75% bonus award miles on eligible flights]
-
A.
mileageAccrual
Indicates the accumulation or earning of mileage (such as distance-based points or credits) as a result of certain actions or usage.
-
B.
mileEarningUnit
Indicates the unit or basis (e.g., per mile, per dollar) used to calculate or award mileage or points in a mileage-earning relationship.
-
C.
loyaltyIncentive
chosen
Indicates a relationship where benefits or rewards are provided to encourage or recognize continued commitment or repeat engagement.
-
D.
loyaltySymbolizedBy
Indicates that an instance of loyalty is represented or expressed by a particular symbol or emblem.
-
E.
airportBenefit
Indicates that one entity gains an advantage, profit, or positive impact from the existence, operation, or services of an airport.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9d2e8488190b2bb8f8509903804 |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb438c481908dff87c47de2f069 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:07 p.m.