Triple
T13130046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nebbiolo |
E311945
|
entity |
| Predicate | grapeSkinThickness |
P2073
|
FINISHED |
| Object | thin-skinned |
—
|
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: thin-skinned | Statement: [Nebbiolo, grapeSkinThickness, thin-skinned]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeSkinThickness Context triple: [Nebbiolo, grapeSkinThickness, thin-skinned]
-
A.
skinThickness
chosen
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
-
B.
grapeMinimum
Indicates the minimum quantity, size, or threshold value associated with grapes in a given context.
-
C.
grapeCondition
Indicates the state or quality of a grape, such as its health, ripeness, or any notable physical condition.
-
D.
grapePriceScale
Indicates how the price of grapes changes in relation to some varying factor, such as quantity, quality, time, or market conditions.
-
E.
grapeColorDistribution
Indicates how colors are proportionally or categorically distributed among a set of grapes.
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9819bfd348190a22d44f837877e1c |
completed | April 10, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69d98043a74c81908648e6cd0b4c7f71 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:07 p.m.