Triple
T9288194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melon de Bourgogne |
E223449
|
entity |
| Predicate | oftenBlendedAs |
P87423
|
FINISHED |
| Object | single-varietal wines |
—
|
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: single-varietal wines | Statement: [Melon de Bourgogne, oftenBlendedAs, single-varietal wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenBlendedAs Context triple: [Melon de Bourgogne, oftenBlendedAs, single-varietal wines]
-
A.
usedInBlends
Indicates that something serves as an ingredient or component within one or more mixtures, combinations, or blends.
-
B.
oftenCrossedOn
Indicates that one entity is frequently traversed or passed over by another entity.
-
C.
typicalBlendStyle
Indicates the usual or characteristic way in which two or more elements are combined or mixed together.
-
D.
typicalBlendShare
Indicates the usual proportion or percentage that one component contributes to a blend relative to the other components.
-
E.
areSometimesGroupedWith
Indicates that two or more entities are occasionally but not consistently classified, treated, or considered together as part of the same group.
- 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_69ca8422ddf881908a3f8f876c9f53aa |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd0862b34c819097cb7c1777313925 |
completed | April 1, 2026, 11:58 a.m. |
| PD | Predicate disambiguation | batch_69cc7a5aeb748190afb89c6bbd2a6d6f |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:35 p.m.