Triple
T6494756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chenin Blanc |
E148128
|
entity |
| Predicate | notableAroma |
P16142
|
FINISHED |
| Object | green apple |
—
|
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: green apple | Statement: [Chenin Blanc, notableAroma, green apple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableAroma Context triple: [Chenin Blanc, notableAroma, green apple]
-
A.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
notableAppellation
Indicates that an entity is known by, or commonly referred to with, a particular notable name or title.
-
C.
notableAria
Indicates that an aria is particularly famous, distinguished, or significant within its artistic or historical context.
-
D.
notableVarieties
Indicates that there are specific, distinguished types or versions associated with an entity that are recognized as notable.
-
E.
notableColor
Indicates that an entity is characteristically or prominently associated with a particular color.
- 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_69c009088f3081909cd467b05919de30 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06ab7c0b8819091437a293b40dfd2 |
completed | March 22, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69c06740bebc81909d9d6956baa2bcb9 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:53 p.m.