Triple
T12347176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crémant de Loire |
E294384
|
entity |
| Predicate | dosageStyles |
P104593
|
FINISHED |
| Object | brut |
—
|
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: brut | Statement: [Crémant de Loire, dosageStyles, brut]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dosageStyles Context triple: [Crémant de Loire, dosageStyles, brut]
-
A.
hasDosageForm
Indicates the specific physical form or presentation in which a drug or medicinal product is supplied or administered (e.g., tablet, injection, cream).
-
B.
typicalDosageCategories
Indicates the standard dosage ranges or categories typically associated with a given treatment, substance, or medication.
-
C.
hasDosingRegimen
Indicates that an entity is associated with a specific dosing regimen, defining how and when a dose is to be administered.
-
D.
doseRegimen
Indicates the specific schedule, frequency, and amount with which a dose of a substance or medication is to be administered.
-
E.
hasHigherDoseStrength
Indicates that one entity has a greater dose strength than another entity in a comparative relationship.
- F. None of above. chosen
Provenance (4 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7ba17481908b03af7316b28d9b |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ecb5efc819086a3530282278bb1 |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d93f607a88819089e89fd263ae9937 |
completed | April 10, 2026, 6:20 p.m. |
Created at: April 8, 2026, 9:53 p.m.