Triple
T12617102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosso Conero |
E301281
|
entity |
| Predicate | maximumSangioveseContent |
P105930
|
FINISHED |
| Object | 15% |
—
|
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: 15% | Statement: [Rosso Conero, maximumSangioveseContent, 15%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumSangioveseContent Context triple: [Rosso Conero, maximumSangioveseContent, 15%]
-
A.
maximumViognierPercentage
Indicates the highest allowable proportion of Viognier in a given wine or blend.
-
B.
minimumVernacciaContent
Indicates the required minimum amount or proportion of Vernaccia content that must be present in a given product or mixture.
-
C.
dominantGrapePercentageRequirement
Indicates the minimum percentage of a single grape variety that must be present in a wine for it to be considered dominant or to meet a specific labeling or classification rule.
-
D.
minimumMalbecPercentage
Indicates the minimum required percentage of Malbec in a composition, blend, or product for a given rule or classification to apply.
-
E.
oenologicalSignificance
Indicates the relationship in which something holds importance, relevance, or notable impact within the context of wine or winemaking.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b195108190ac25bd95e644ace4 |
completed | April 10, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69d96179c7648190a05a13991d62bebb |
completed | April 10, 2026, 8:45 p.m. |
Created at: April 9, 2026, 5:13 p.m.