Triple
T30033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabernet Sauvignon |
E599
|
entity |
| Predicate | foodPairing |
P162
|
FINISHED |
| Object | red meat |
—
|
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: red meat | Statement: [Cabernet Sauvignon, foodPairing, red meat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: foodPairing Context triple: [Cabernet Sauvignon, foodPairing, red meat]
-
A.
diet
Indicates that an entity regularly consumes a particular type or range of food as its primary source of nutrition.
-
B.
traditionalCuisine
Indicates that an entity is associated with the customary or historically rooted style of cooking and food preparation characteristic of a particular culture, region, or community.
-
C.
servesAtThePleasureOf
Indicates that one entity holds a position or role that can be terminated at any time by another entity, typically at the discretion or will of that other entity.
-
D.
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).
-
E.
complements
chosen
Indicates that one entity enhances, completes, or improves another by providing qualities or functions that fit well together.
- 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a2490d80a0819083bf604c1229e903 |
completed | Feb. 28, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69a2486eb01881909241540dda28e1ff |
completed | Feb. 28, 2026, 1:44 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.