Triple
T14487243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limnio wine |
E359263
|
entity |
| Predicate | hasAromaNotes |
P23045
|
FINISHED |
| Object | red 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 fruits | Statement: [Limnio wine, hasAromaNotes, red fruits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAromaNotes Context triple: [Limnio wine, hasAromaNotes, red fruits]
-
A.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
secondaryAroma
Indicates that an entity has a secondary or supporting aroma characteristic in addition to its primary scent.
-
C.
coffeeFlavorNotes
Indicates the characteristic taste and aroma qualities associated with a particular coffee, such as specific flavor notes perceived when it is consumed.
-
D.
primaryAroma
Indicates the main or most dominant scent associated with an entity, distinguishing it from secondary or background aromas.
-
E.
hasFragrance
chosen
Indicates that one entity possesses or emits a particular scent or aroma associated with another 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de924ee0f08190baf68318b41fa64d |
completed | April 14, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69de5c487b4c819097803e58dca628a5 |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:20 a.m.