Triple
T18786708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bardolino |
E459393
|
entity |
| Predicate | winePairing |
P133011
|
FINISHED |
| Object | pasta dishes |
—
|
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: pasta dishes | Statement: [Bardolino, winePairing, pasta dishes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winePairing Context triple: [Bardolino, winePairing, pasta dishes]
-
A.
wineComponent
Indicates that one entity is a constituent ingredient or part of a wine represented by the other entity.
-
B.
sweetWineSuitability
Indicates the degree to which something is appropriate or recommended for pairing with or serving as a sweet wine.
-
C.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
D.
wineServingSuggestion
Indicates the recommended way or context in which a particular wine is best served or enjoyed.
-
E.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
- 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e597828cb481908fe569747f816e15 |
completed | April 20, 2026, 3:03 a.m. |
| PD | Predicate disambiguation | batch_69e48d16dd34819096e096d0c0e4c15c |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e49785fd7081909577e90a55df0a35 |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 11:52 a.m.