Triple
T14406382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peloursin |
E357206
|
entity |
| Predicate | wineColorContribution |
P102176
|
FINISHED |
| Object | high color intensity |
—
|
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: high color intensity | Statement: [Peloursin, wineColorContribution, high color intensity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineColorContribution Context triple: [Peloursin, wineColorContribution, high color intensity]
-
A.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
B.
wineColorMajority
Indicates that the majority of a given set or collection of wines share the same color.
-
C.
wineStyleContribution
chosen
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
-
D.
wineColorNotAllowed
Indicates that a particular wine color is not permitted or is disallowed in the given context or relationship.
-
E.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de908804048190a4fe58afc2e0a5b6 |
completed | April 14, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69de2aa1b57881909a033eac8545c417 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.