Triple
T18387769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crémant d’Alsace |
E446636
|
entity |
| Predicate | dosageLevel |
P130936
|
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 d’Alsace, dosageLevel, brut]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dosageLevel Context triple: [Crémant d’Alsace, dosageLevel, brut]
-
A.
dosageAdded
Indicates that an additional amount of a substance or medication has been administered or incorporated into an existing dosage.
-
B.
dosageStyle
Indicates the manner or pattern in which a dose of a substance (such as a medication) is administered or taken.
-
C.
dosageStyles
Indicates the various ways or formats in which a dosage (amount and schedule of administration) is specified or presented for a treatment or medication.
-
D.
hasHigherDoseStrength
Indicates that one entity has a greater dose strength than another entity in a comparative relationship.
-
E.
hasDoseUnit
Indicates the unit of measurement in which a specified dose or quantity of a substance is expressed.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e517a114c08190af1be1ae52c83b63 |
completed | April 19, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69e44ff1f92c8190afbb8e85d12bf2a9 |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a1bda48190a9cd1db436d4be62 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:46 a.m.