Triple
T14815780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francs Côtes de Bordeaux AOC |
E348309
|
entity |
| Predicate | typicalRedWineTannins |
P2069
|
FINISHED |
| Object | supple tannins |
—
|
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: supple tannins | Statement: [Francs Côtes de Bordeaux AOC, typicalRedWineTannins, supple tannins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRedWineTannins Context triple: [Francs Côtes de Bordeaux AOC, typicalRedWineTannins, supple tannins]
-
A.
tanninLevel
chosen
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
B.
grapeColorForReds
Indicates that the predicate specifies the typical color of grapes used to produce red wines.
-
C.
typicalAlcoholRangeRed
Indicates that the subject red wine typically falls within a specified range of alcohol content.
-
D.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
E.
typicalBlendCabernetFrancPercentage
Indicates the percentage of Cabernet Franc that is typically included in a particular wine blend.
- 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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decfe2c1ec81908b3dff7a5d0e85d0 |
completed | April 14, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:49 a.m.