Triple
T10074461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jurançon wine region |
E213713
|
entity |
| Predicate | grapeCharacteristic |
P9793
|
FINISHED |
| Object | high natural acidity |
—
|
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 natural acidity | Statement: [Jurançon wine region, grapeCharacteristic, high natural acidity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeCharacteristic Context triple: [Jurançon wine region, grapeCharacteristic, high natural acidity]
-
A.
viticulturalCharacteristic
chosen
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
B.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
C.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
D.
fruitCharacteristic
Indicates that a specified characteristic or property is attributed to a particular fruit.
-
E.
grapeCondition
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_69ca839add308190b57d53b4ec21f2d0 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd017b8288190a577bd66e4ba66b7 |
completed | April 2, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 8:59 p.m.