Triple
T16545096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bandol |
E401920
|
entity |
| Predicate | typicalRoséWineCharacter |
P16142
|
FINISHED |
| Object | structured |
—
|
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: structured | Statement: [Bandol, typicalRoséWineCharacter, structured]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRoséWineCharacter Context triple: [Bandol, typicalRoséWineCharacter, structured]
-
A.
typicalRedWineBody
Indicates that a wine exhibits the characteristic body (weight and mouthfeel) typically associated with red wines.
-
B.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
C.
typicalRedBlendProfile
Indicates that something exhibits the characteristic flavor, aroma, and structural profile commonly associated with a standard red wine blend.
-
D.
grapeColorForRosé
Indicates that a particular grape color is used in the production of rosé wine.
-
E.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34560daf08190b353b415d8ab280d |
completed | April 18, 2026, 8:48 a.m. |
| PD | Predicate disambiguation | batch_69e2969fab208190ad64164d24748c45 |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:15 a.m.