Triple
T524068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Wright |
E10878
|
entity |
| Predicate | featuresSport |
P6214
|
FINISHED |
| Object | basketball |
—
|
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: basketball | Statement: [Just Wright, featuresSport, basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresSport Context triple: [Just Wright, featuresSport, basketball]
-
A.
sportFocus
chosen
Indicates that one entity has a primary emphasis, specialization, or concentration on a particular sport represented by the other entity.
-
B.
featuredCompetition
Indicates that a particular competition is highlighted or given special prominence relative to others.
-
C.
popularSport
Indicates that a sport is widely liked, followed, or played by many people within a certain group or region.
-
D.
sportCategory
Indicates that one entity is classified as a type or category of sport to which the other entity (typically a specific sport or sporting event) belongs.
-
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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1b656c08190b387f21b06d99e68 |
completed | Feb. 28, 2026, 1:46 p.m. |
| PD | Predicate disambiguation | batch_69a2f018129c81909494450fcba71b59 |
completed | Feb. 28, 2026, 1:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.