Triple
T9604957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercurey AOC |
E231944
|
entity |
| Predicate | typicalAlcoholRangeWhite |
P69107
|
FINISHED |
| Object | 12.0–13.5% ABV |
—
|
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: 12.0–13.5% ABV | Statement: [Mercurey AOC, typicalAlcoholRangeWhite, 12.0–13.5% ABV]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAlcoholRangeWhite Context triple: [Mercurey AOC, typicalAlcoholRangeWhite, 12.0–13.5% ABV]
-
A.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
B.
bottlingStrengthRange
chosen
Indicates the range of alcohol strengths at which a beverage is bottled.
-
C.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
-
D.
madeWithAlcohol
Indicates that something is created, prepared, or produced using alcohol as an ingredient or component.
-
E.
isAlcoholicBeverage
Indicates that a beverage contains alcohol and is classified as an alcoholic drink.
- 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_69ca8484838c8190b2049199d22fef70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a5e4a7c8190830b5ad9762ece46 |
completed | April 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a6fd2481908efd131e207b8143 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:08 p.m.