Triple
T9492120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montagny |
E228915
|
entity |
| Predicate | numberOfPremierCruClimats |
P88917
|
FINISHED |
| Object | over 40 |
—
|
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: over 40 | Statement: [Montagny, numberOfPremierCruClimats, over 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPremierCruClimats Context triple: [Montagny, numberOfPremierCruClimats, over 40]
-
A.
notablePremierCru
Indicates that a subject is recognized as a distinguished or especially noteworthy Premier Cru within its classification.
-
B.
hasGrandCru
Indicates that an entity possesses, is associated with, or includes a wine classified as Grand Cru.
-
C.
maximumViognierPercentage
Indicates the highest allowable proportion of Viognier in a given wine or blend.
-
D.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
E.
positionInCôteDeBeaune
Indicates that something is located within or belongs to the Côte de Beaune subregion.
- 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_69ca847424f081908180305555139f7a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd80ca26bc819084add20027aea4a5 |
completed | April 1, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69cca5651a588190a3cfebe249a223e5 |
completed | April 1, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69cca8c6b0f081908334d6c7cf80e03c |
completed | April 1, 2026, 5:10 a.m. |
Created at: March 30, 2026, 7:56 p.m.