Triple
T11010111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gamay for red and rosé wines |
E260222
|
entity |
| Predicate | roséStyle |
P51955
|
FINISHED |
| Object | pale pink and fruity |
—
|
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: pale pink and fruity | Statement: [Gamay for red and rosé wines, roséStyle, pale pink and fruity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roséStyle Context triple: [Gamay for red and rosé wines, roséStyle, pale pink and fruity]
-
A.
roséWineAllowed
Indicates that the consumption or presence of rosé wine is permitted in the given context or under the specified conditions.
-
B.
roséWineProductionAllowed
Indicates that the production of rosé wine is permitted under the relevant rules or conditions.
-
C.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
D.
typicalRoséCharacter
chosen
Indicates that something exhibits the characteristic qualities or flavor profile commonly associated with rosé.
-
E.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an 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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79788d44c819084f35693ed96f422 |
completed | April 9, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d72e96be6c8190a46c69f61b2d8cd4 |
completed | April 9, 2026, 4:44 a.m. |
Created at: April 8, 2026, 9:25 p.m.