Triple
T27085459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muskateller |
E686023
|
entity |
| Predicate | hasViticulturalCharacteristic |
P42963
|
FINISHED |
| Object | early to mid ripening |
—
|
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: early to mid ripening | Statement: [Muskateller, hasViticulturalCharacteristic, early to mid ripening]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasViticulturalCharacteristic Context triple: [Muskateller, hasViticulturalCharacteristic, early to mid ripening]
-
A.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
B.
viticulturalFeature
chosen
Indicates a characteristic, condition, or attribute specifically related to grape growing or vineyard cultivation.
-
C.
hasViticulturalImportance
Indicates that something plays a significant role or has notable relevance in the cultivation and production of grapes for wine.
-
D.
alsoUsesGrapeVariety
Indicates that one entity, in addition to another, makes use of the same grape variety in its composition or production.
-
E.
hasVineyardType
Indicates the specific type or classification of a vineyard associated with an entity.
- 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f676c440708190a4b9974e95d2291a |
completed | May 2, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69f675fd59608190b246383435e68fce |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 27, 2026, 8:37 a.m.