Triple
T11914386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ladoix AOC |
E283475
|
entity |
| Predicate | minimumAlcoholRed |
P102323
|
FINISHED |
| Object | 10.5 percent by volume |
—
|
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: 10.5 percent by volume | Statement: [Ladoix AOC, minimumAlcoholRed, 10.5 percent by volume]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumAlcoholRed Context triple: [Ladoix AOC, minimumAlcoholRed, 10.5 percent by volume]
-
A.
typicalAlcoholRangeRed
Indicates that the subject red wine typically falls within a specified range of alcohol content.
-
B.
higherMinimumAlcoholThan
Indicates that the minimum required or actual alcohol content of one entity is greater than that of another entity.
-
C.
redWineRequired
Indicates that red wine is needed or mandated in the given context or situation.
-
D.
minimumAlcoholByVolumeRiserva
Indicates the minimum required alcohol by volume percentage that a product must have to qualify as a "Riserva" version.
-
E.
grapeMinimum
Indicates the minimum quantity, size, or threshold value associated with grapes in a given context.
- F. None of above. chosen
Provenance (4 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e52a2bc08190b80cc6ccf5779d7c |
completed | April 10, 2026, 11:55 a.m. |
| PD | Predicate disambiguation | batch_69d8bb3632ac8190b13e53c2b5db7125 |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:44 p.m.