Triple
T2504009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regal Crown Club |
E52534
|
entity |
| Predicate | pointsAccrualMethod |
P32728
|
FINISHED |
| Object | points earned per ticket purchase |
—
|
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: points earned per ticket purchase | Statement: [Regal Crown Club, pointsAccrualMethod, points earned per ticket purchase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsAccrualMethod Context triple: [Regal Crown Club, pointsAccrualMethod, points earned per ticket purchase]
-
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.
awardedFrequency
Indicates how often an award or recognition is given within a specified time period.
-
C.
loyaltyProgramType
Indicates the specific category or kind of loyalty program associated with an entity (such as points-based, tiered, or subscription-based).
-
D.
mileEarningUnit
chosen
Indicates the unit or basis (e.g., per mile, per dollar) used to calculate or award mileage or points in a mileage-earning relationship.
-
E.
creditSchedule
Indicates a planned or agreed-upon timetable for extending, using, or repaying credit between parties.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1cd2db0819087d21ec49ffd9585 |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0bba5348190bb4637d3165cb339 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.