Triple
T16174177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grenache-Syrah-Mourvèdre |
E392518
|
entity |
| Predicate | GrenacheContribution |
P102176
|
FINISHED |
| Object | alcohol richness |
—
|
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: alcohol richness | Statement: [Grenache-Syrah-Mourvèdre, GrenacheContribution, alcohol richness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: GrenacheContribution Context triple: [Grenache-Syrah-Mourvèdre, GrenacheContribution, alcohol richness]
-
A.
wineStyleContribution
chosen
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
-
B.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
C.
grapeProduct
Indicates that one entity is a product derived from or made using grapes.
-
D.
wineVariety
Indicates the specific type or variety of wine associated with an entity.
-
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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb9b8208190b60874cec7a3a98e |
completed | April 17, 2026, 11:51 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.