Triple
T2825118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Côte d'Or |
E54900
|
entity |
| Predicate | typicalRedWineProfile |
P16142
|
FINISHED |
| Object | medium-bodied with red fruit and earthy notes |
—
|
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-bodied with red fruit and earthy notes | Statement: [Côte d'Or, typicalRedWineProfile, medium-bodied with red fruit and earthy notes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRedWineProfile Context triple: [Côte d'Or, typicalRedWineProfile, medium-bodied with red fruit and earthy notes]
-
A.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
C.
redWinesLabeledAs
Indicates that certain red wines are designated or identified by a particular label or labeling term.
-
D.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
E.
traditionalGrapeVariety
Indicates that a grape variety is traditionally or historically used in a specific region, wine style, or cultural winemaking practice.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.