Triple
T17495996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanquette Méthode Ancestrale |
E426060
|
entity |
| Predicate | alcoholContentCharacteristic |
P124347
|
FINISHED |
| Object | low alcohol |
—
|
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: low alcohol | Statement: [Blanquette Méthode Ancestrale, alcoholContentCharacteristic, low alcohol]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alcoholContentCharacteristic Context triple: [Blanquette Méthode Ancestrale, alcoholContentCharacteristic, low alcohol]
-
A.
featuresAlcoholReference
Indicates that the subject includes or contains a reference to alcohol or alcoholic beverages.
-
B.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
C.
alcoholStrengthCategory
chosen
Indicates the classification of an alcoholic beverage based on the strength or concentration of its alcohol content.
-
D.
isAlcoholicBeverage
Indicates that a beverage contains alcohol and is classified as an alcoholic drink.
-
E.
wineAlcoholPotential
Indicates the potential alcohol content that a wine could reach based on its current sugar level or fermentation stage.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4520e9c8c8190aa955766bc915d26 |
completed | April 19, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.