Triple
T30001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabernet Sauvignon |
E599
|
entity |
| Predicate | agingPotential |
P2072
|
FINISHED |
| Object | high |
—
|
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 | Statement: [Cabernet Sauvignon, agingPotential, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: agingPotential Context triple: [Cabernet Sauvignon, agingPotential, high]
-
A.
attracts
Indicates that one entity exerts a force or influence that draws another entity toward it.
-
B.
notableEngagement
Indicates a significant interaction, involvement, or participation between entities that is noteworthy or distinguished in some context.
-
C.
isPopularWith
Indicates that one entity is well-liked, favored, or widely accepted by another entity or group.
-
D.
representsInterestOf
Indicates that one entity expresses, holds, or embodies an interest, concern, or stake in another entity or subject.
-
E.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
- 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_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. |
| PDg | Predicate description generation | batch_69a2490c9c348190bf8536a08415b94a |
completed | Feb. 28, 2026, 1:46 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.