Triple
T30091793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ciliegiolo |
E764752
|
entity |
| Predicate | wineAromaDescriptor |
P152625
|
FINISHED |
| Object | fresh cherry |
—
|
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: fresh cherry | Statement: [Ciliegiolo, wineAromaDescriptor, fresh cherry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineAromaDescriptor Context triple: [Ciliegiolo, wineAromaDescriptor, fresh cherry]
-
A.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
characteristicAroma
chosen
Indicates that one entity has a distinctive smell or scent that characterizes or is typically associated with another entity.
-
C.
notableFlavorNotes
Indicates that something is characterized by specific, distinguishable flavor notes that are especially prominent or noteworthy.
-
D.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
E.
primaryAroma
Indicates the main or most dominant scent associated with an entity, distinguishing it from secondary or background aromas.
- 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67d7064188190a7cc837036437d1b |
completed | May 2, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f673c664f08190b4d66cdc305e10db |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 7:06 p.m.