Triple
T25556882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AdvoCare V100 Texas Bowl |
E640594
|
entity |
| Predicate | sponsorBusinessType |
P2589
|
FINISHED |
| Object | multi-level marketing company |
—
|
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: multi-level marketing company | Statement: [AdvoCare V100 Texas Bowl, sponsorBusinessType, multi-level marketing company]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorBusinessType Context triple: [AdvoCare V100 Texas Bowl, sponsorBusinessType, multi-level marketing company]
-
A.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
B.
sponsorBrandType
Indicates the type or category of brand that is acting as a sponsor in the relationship.
-
C.
eligibleBusinessType
Indicates that a business entity qualifies under specified criteria to be considered an eligible type for a particular program, rule, or context.
-
D.
sponsorIndustry
Indicates that an entity acts as a sponsor for, or is financially or organizationally supporting, a particular industry or industrial sector.
-
E.
sponsoringOrganizationType
chosen
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or 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_69e75dc101a881909fd33b02174e9768 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f8ca2cf48190997cd68875571217 |
completed | May 2, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 21, 2026, 3:40 p.m.