Triple
T29999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabernet Sauvignon |
E599
|
entity |
| Predicate | acidityLevel |
P2070
|
FINISHED |
| Object | medium to 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: medium to high | Statement: [Cabernet Sauvignon, acidityLevel, medium to high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: acidityLevel Context triple: [Cabernet Sauvignon, acidityLevel, medium to high]
-
A.
hasSalinityRange
Indicates the range of salinity values within which something (such as a substance, environment, or organism) is present, applicable, or able to function.
-
B.
hasValley
Indicates that one entity contains, features, or is characterized by the presence of a valley associated with the other entity.
-
C.
elevation
Indicates the vertical height or altitude of one entity relative to a reference level or another entity.
-
D.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
E.
volume
Indicates the amount of three-dimensional space an entity occupies or contains.
- 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.