Triple
T11808756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uzooma Okeke |
E280814
|
entity |
| Predicate | careerFocus |
P24248
|
FINISHED |
| Object | Canadian football |
—
|
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: Canadian football | Statement: [Uzooma Okeke, careerFocus, Canadian football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerFocus Context triple: [Uzooma Okeke, careerFocus, Canadian football]
-
A.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
-
B.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
C.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
D.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire career.
-
E.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.