Triple
T12144361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Givry |
E289276
|
entity |
| Predicate | typicalAromasWhite |
P71397
|
FINISHED |
| Object | citrus |
—
|
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: citrus | Statement: [Givry, typicalAromasWhite, citrus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAromasWhite Context triple: [Givry, typicalAromasWhite, citrus]
-
A.
typicalOakAroma
Indicates that something has the characteristic smell commonly associated with oak wood or oak aging.
-
B.
primaryAroma
Indicates the main or most dominant scent associated with an entity, distinguishing it from secondary or background aromas.
-
C.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
D.
secondaryAroma
chosen
Indicates that an entity has a secondary or supporting aroma characteristic in addition to its primary scent.
-
E.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
- 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_69d6ab4c6710819097a9d228382dde43 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91841615c819097f20a7447a1b8f4 |
completed | April 10, 2026, 3:33 p.m. |
| PD | Predicate disambiguation | batch_69d91508f8008190b3a90ec0bf0953ca |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.