Triple
T9604956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercurey AOC |
E231944
|
entity |
| Predicate | typicalAlcoholRangeRed |
P89196
|
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, typicalAlcoholRangeRed, 12.0–13.5% ABV]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAlcoholRangeRed Context triple: [Mercurey AOC, typicalAlcoholRangeRed, 12.0–13.5% ABV]
-
A.
redWinesLabeledAs
Indicates that certain red wines are designated or identified by a particular label or labeling term.
-
B.
roséWineAllowed
Indicates that the consumption or presence of rosé wine is permitted in the given context or under the specified conditions.
-
C.
grapeColorForReds
Indicates that the predicate specifies the typical color of grapes used to produce red wines.
-
D.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
E.
redWineProductionAllowed
Indicates that producing red wine is permitted under the relevant rules, conditions, or regulations.
- 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_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. |
| PDg | Predicate description generation | batch_69ccd93fc45c8190a823305e461e581d |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:08 p.m.