Triple
T25809926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tims Rewards |
E650077
|
entity |
| Predicate | rewardTrigger |
P80047
|
FINISHED |
| Object | qualifying 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: qualifying purchase | Statement: [Tims Rewards, rewardTrigger, qualifying purchase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rewardTrigger Context triple: [Tims Rewards, rewardTrigger, qualifying purchase]
-
A.
rewardSignal
Indicates that one entity provides a signal representing feedback or incentive (such as a reward or penalty) to guide another entity’s behavior or learning process.
-
B.
rewardUse
Indicates that one entity grants or provides a reward in response to the use or utilization of another entity.
-
C.
rewardMechanism
chosen
Indicates a relationship where an entity provides or defines a system of incentives or compensation in response to certain actions, behaviors, or outcomes.
-
D.
rewardScope
Indicates the extent, range, or conditions under which a reward applies within a given context or system.
-
E.
rewardUnit
Indicates that one entity serves as the unit or measure in which a reward is quantified or granted to another entity.
- 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_69e7ab35d264819095367f7e80c983ff |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f600c37d40819086cc056057c25629 |
completed | May 2, 2026, 1:48 p.m. |
| PD | Predicate disambiguation | batch_69f4938b960081909b53c074a3e0c7c2 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 7:08 a.m.