Triple
T12463420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Cru villages |
E297857
|
entity |
| Predicate | includesVillage |
P4011
|
FINISHED |
| Object | Verzenay |
E346841
|
NE 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: Verzenay | Statement: [Grand Cru villages, includesVillage, Verzenay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verzenay Context triple: [Grand Cru villages, includesVillage, Verzenay]
-
A.
Verzenay
chosen
Verzenay is a renowned Grand Cru wine-producing village in France’s Champagne region, particularly noted for its Pinot Noir vineyards on the Montagne de Reims.
-
B.
Santenay
Santenay is a wine-producing village in Burgundy, France, known for its predominantly red wines made from Pinot Noir and its location at the southern end of the Côte de Beaune.
-
C.
Valtournenche
Valtournenche is a mountain village and commune in Italy’s Aosta Valley, known as a gateway to the Matterhorn and a popular destination for alpine climbing and skiing.
-
D.
Saint-Véran
Saint-Véran is a French wine appellation in southern Burgundy known for its high-quality Chardonnay-based white wines.
-
E.
Cugny
Cugny is a locality within the municipality of Bernex in the canton of Geneva, Switzerland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94db5efe88190a76949e4ddc3314c |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8bc820c8190b6e54a381621fdc8 |
completed | May 3, 2026, 2:53 a.m. |
Created at: April 8, 2026, 9:56 p.m.