Triple
T12239174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grenache |
E291680
|
entity |
| Predicate | wineColorUse |
P52137
|
FINISHED |
| Object | key grape for rosé in Provence |
—
|
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: key grape for rosé in Provence | Statement: [Grenache, wineColorUse, key grape for rosé in Provence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineColorUse Context triple: [Grenache, wineColorUse, key grape for rosé in Provence]
-
A.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
B.
wineColorNotAllowed
Indicates that a particular wine color is not permitted or is disallowed in the given context or relationship.
-
C.
usesWineType
chosen
Indicates that one entity makes use of, incorporates, or is associated with a particular type or category of wine.
-
D.
grapeColorProduced
Indicates the color that is produced by or characteristic of a given grape.
-
E.
wineStyleContribution
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d924a3973c8190a882046963b320fb |
completed | April 10, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69d91c41bcbc81909782f4e3c571b218 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.