Triple
T35844140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nordy Club loyalty program |
E1036162
|
entity |
| Predicate | pointsEarnedFor |
P187428
|
FINISHED |
| Object | eligible Nordstrom 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: eligible Nordstrom purchases | Statement: [Nordy Club loyalty program, pointsEarnedFor, eligible Nordstrom purchases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsEarnedFor Context triple: [Nordy Club loyalty program, pointsEarnedFor, eligible Nordstrom purchases]
-
A.
pointsEarnedFrom
Indicates the number of points that an entity has received as a result of another specified source, action, or event.
-
B.
pointsAwarded
Indicates that a specified number of points has been granted to an entity as a result of some action or event.
-
C.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
D.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
-
E.
careerPoints
Indicates the total number of points an individual has accumulated over the course of their entire career in a given activity or domain.
- F. None of above. chosen
Provenance (4 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_69f76e1a29e8819088280f26096aeb55 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69fb563a28d88190b28345c465c545f8 |
completed | May 6, 2026, 2:54 p.m. |
Created at: May 3, 2026, 4:06 p.m.