Triple
T13612788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PC Optimum |
E325234
|
entity |
| Predicate | pointsEarnedOn |
P62973
|
FINISHED |
| Object | eligible 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 purchases | Statement: [PC Optimum, pointsEarnedOn, eligible purchases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsEarnedOn Context triple: [PC Optimum, pointsEarnedOn, eligible purchases]
-
A.
pointsEarnedFrom
chosen
Indicates the number of points that an entity has received as a result of another specified source, action, or event.
-
B.
careerPoints
Indicates the total number of points an individual has accumulated over the course of their entire career in a given activity or domain.
-
C.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
D.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
E.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae1b3ee481909bd43ded6227a3e5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:50 p.m.