Triple
T37041835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quinton de Kock |
E916798
|
entity |
| Predicate | careerSpecialization |
P24248
|
FINISHED |
| Object | limited-overs cricket |
—
|
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: limited-overs cricket | Statement: [Quinton de Kock, careerSpecialization, limited-overs cricket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerSpecialization Context triple: [Quinton de Kock, careerSpecialization, limited-overs cricket]
-
A.
subjectSpecialization
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
-
B.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
-
C.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
D.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
E.
laterSpecializedIn
Indicates that an entity initially engaged in a broader or different field and subsequently focused its work or expertise in a more specific or specialized area.
- 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_69f76e93ec4c8190be81cf87354d9155 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:14 p.m.