Triple
T11820776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milk Cup |
E281120
|
entity |
| Predicate | sponsorProduct |
P101681
|
FINISHED |
| Object | milk |
—
|
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: milk | Statement: [Milk Cup, sponsorProduct, milk]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorProduct Context triple: [Milk Cup, sponsorProduct, milk]
-
A.
sponsorFrom
Indicates that one entity provides sponsorship or financial backing originating from a specified source entity.
-
B.
sponsorEnd
Indicates the point in time or condition at which a sponsorship relationship or sponsorship-related action comes to an end.
-
C.
sponsorBrandType
Indicates the type or category of brand that is acting as a sponsor in the relationship.
-
D.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
E.
sponsorTo
Indicates that one entity provides support, funding, or endorsement to another entity, typically to enable or promote the latter’s activities or initiatives.
- F. None of above. chosen
Provenance (4 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_69d8a5e87e488190905bc3bb6d721e56 |
completed | April 10, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69d8a251fc08819095933f1d13c3b742 |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a43cc0c881909fed7cd759fe90b1 |
completed | April 10, 2026, 7:18 a.m. |
Created at: April 8, 2026, 9:42 p.m.