Triple
T2441297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doux |
E53280
|
entity |
| Predicate | hasConsumerUse |
P19962
|
FINISHED |
| Object | for those who prefer very sweet wines |
—
|
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: for those who prefer very sweet wines | Statement: [Doux, hasConsumerUse, for those who prefer very sweet wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConsumerUse Context triple: [Doux, hasConsumerUse, for those who prefer very sweet wines]
-
A.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
B.
hasUseCase
chosen
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
C.
isUsedUnder
Indicates that one entity is utilized or applied within the context, conditions, or framework defined by another entity.
-
D.
canUse
Indicates that one entity has the ability, permission, or suitability to make use of another entity or resource.
-
E.
usedSupport
Indicates that one entity employed or relied on another entity as a means of support or assistance in performing an action or achieving a result.
- 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_69ab495b6dac8190ac82661aa1452222 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcebf7cac8190889e6890d72c256c |
completed | March 7, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69abc5ac11b081908ce6a506e81a742a |
completed | March 7, 2026, 6:29 a.m. |
Created at: March 6, 2026, 9:43 p.m.