Triple
T17496014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanquette Méthode Ancestrale |
E426060
|
entity |
| Predicate | carbonationLevelCharacteristic |
P84042
|
FINISHED |
| Object | gentle mousse |
—
|
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: gentle mousse | Statement: [Blanquette Méthode Ancestrale, carbonationLevelCharacteristic, gentle mousse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carbonationLevelCharacteristic Context triple: [Blanquette Méthode Ancestrale, carbonationLevelCharacteristic, gentle mousse]
-
A.
carbonation
Indicates that a substance, typically a beverage, has been infused with carbon dioxide gas, resulting in bubbles or fizziness.
-
B.
carbonationDescription
chosen
Indicates the description of the level, style, or characteristics of carbonation present in a beverage.
-
C.
carbonationSource
Indicates the source or method by which something becomes carbonated (i.e., how carbon dioxide is introduced).
-
D.
hasCarbonatedWater
Indicates that an entity contains or is associated with carbonated (fizzy) water as a component or ingredient.
-
E.
stillOrSparkling
Indicates whether something, typically a beverage, is non-carbonated (still) or carbonated (sparkling).
- 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.