Triple
T35402576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanley |
E1023272
|
entity |
| Predicate | hasSponsorshipCategory |
P58189
|
FINISHED |
| Object | primary car sponsorship in NASCAR |
—
|
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: primary car sponsorship in NASCAR | Statement: [Stanley, hasSponsorshipCategory, primary car sponsorship in NASCAR]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSponsorshipCategory Context triple: [Stanley, hasSponsorshipCategory, primary car sponsorship in NASCAR]
-
A.
hasSponsor
Indicates that one entity financially or otherwise supports another entity, typically in exchange for recognition or other benefits.
-
B.
hasSupportCategory
chosen
Indicates that an entity is associated with a particular type or category of support it receives or provides.
-
C.
hasCompetitionCategory
Indicates that an entity is associated with a specific category or division within a competition.
-
D.
hasSponsorshipRegion
Indicates the geographic region or area for which a sponsorship is applicable or assigned.
-
E.
includesSponsorship
Indicates that one entity’s involvement, agreement, or arrangement explicitly involves sponsorship provided by another entity.
- 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_69f76df43ca4819098711ca4370f1bb9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe8ddf70e48190a917eb9e8f7b6966 |
completed | May 9, 2026, 1:29 a.m. |
| PD | Predicate disambiguation | batch_69fe87ef94dc81909bb00ec8d6de9bcd |
completed | May 9, 2026, 1:03 a.m. |
Created at: May 3, 2026, 4:03 p.m.