Triple
T36394547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erica Ogwumike |
E896431
|
entity |
| Predicate | sportNumberOfPointsSpecialty |
P185431
|
FINISHED |
| Object | scoring |
—
|
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: scoring | Statement: [Erica Ogwumike, sportNumberOfPointsSpecialty, scoring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportNumberOfPointsSpecialty Context triple: [Erica Ogwumike, sportNumberOfPointsSpecialty, scoring]
-
A.
sportNumber
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
-
B.
sportsCount
Indicates the number of sports associated with or involved in a given entity or context.
-
C.
hasSportSpecialty
Indicates that an entity has a particular sport in which it specializes or is primarily associated.
-
D.
tournamentSpecific
Indicates that the relationship, condition, or data applies only within the context of a particular tournament and not generally across other contexts or events.
-
E.
sportFocus
Indicates that one entity has a primary emphasis, specialization, or concentration on a particular sport represented by the other entity.
- 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_69f76e52e3108190becf70b090ae7bd6 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bcccd7988190aa5c931ff347d33c |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be9b9ab481908328e0e8d8ac73d4 |
completed | May 3, 2026, 9:31 p.m. |
Created at: May 3, 2026, 4:10 p.m.