Triple
T19341873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verduzzo Friulano |
E483773
|
entity |
| Predicate | grapeSkinCharacteristic |
P88645
|
FINISHED |
| Object | relatively thick skins |
—
|
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: relatively thick skins | Statement: [Verduzzo Friulano, grapeSkinCharacteristic, relatively thick skins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeSkinCharacteristic Context triple: [Verduzzo Friulano, grapeSkinCharacteristic, relatively thick skins]
-
A.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
B.
grapeProduct
Indicates that one entity is a product derived from or made using grapes.
-
C.
fruitCharacteristic
Indicates that a specified characteristic or property is attributed to a particular fruit.
-
D.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
E.
grapeCondition
chosen
Indicates the state or quality of a grape, such as its health, ripeness, or any notable physical condition.
- 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6185795bc8190a63061ca794c0d67 |
completed | April 20, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:33 p.m.