Triple
T9774813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel William Carter |
E237219
|
entity |
| Predicate | sportNumberOfPoints |
P49539
|
FINISHED |
| Object | 1598 test points for New Zealand |
—
|
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: 1598 test points for New Zealand | Statement: [Daniel William Carter, sportNumberOfPoints, 1598 test points for New Zealand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportNumberOfPoints Context triple: [Daniel William Carter, sportNumberOfPoints, 1598 test points for New Zealand]
-
A.
sportNumber
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
-
B.
pointsScored
chosen
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
C.
JetsScore
Indicates that the team named Jets scores a certain number of points in a game or event.
-
D.
totalPointsScored
Indicates the total number of points accumulated or scored by an entity over a defined period, event, or context.
-
E.
sportsCount
Indicates the number of sports associated with or involved in a given entity or context.
- 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_69ca84d975a08190aab25b02a89bdab3 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda13148288190bcbb3b4a066d9fc1 |
completed | April 1, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69cd03d3b68c81909e570401a891b9f2 |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:26 p.m.