Triple
T35656401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nizza DOCG |
E1030297
|
entity |
| Predicate | typicalAromaDescriptors |
P71396
|
FINISHED |
| Object | red and black fruits |
—
|
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: red and black fruits | Statement: [Nizza DOCG, typicalAromaDescriptors, red and black fruits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAromaDescriptors Context triple: [Nizza DOCG, typicalAromaDescriptors, red and black fruits]
-
A.
characteristicAroma
Indicates that one entity has a distinctive smell or scent that characterizes or is typically associated with another entity.
-
B.
typicalAromaIntensity
Indicates the usual strength or level of aroma typically associated with something.
-
C.
typicalOakAroma
Indicates that something has the characteristic smell commonly associated with oak wood or oak aging.
-
D.
primaryAroma
chosen
Indicates the main or most dominant scent associated with an entity, distinguishing it from secondary or background aromas.
-
E.
commonScents
Indicates that two or more entities share the same or very similar smells or fragrances.
- 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_69f76e0938088190a8f199631e97dec3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:05 p.m.