Triple
T244721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinot Noir |
E5011
|
entity |
| Predicate | primaryRegion |
P1103
|
FINISHED |
| Object | Burgundy |
E6890
|
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: Burgundy | Statement: [Pinot Noir, primaryRegion, Burgundy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burgundy Context triple: [Pinot Noir, primaryRegion, Burgundy]
-
A.
Burgundy
chosen
Burgundy is a renowned wine-producing region in eastern France, famous for its high-quality Chardonnay and Pinot Noir wines.
-
B.
Chablis
Chablis is a renowned wine region in northern Burgundy, France, famous for its crisp, mineral-driven white wines made exclusively from Chardonnay grapes.
-
C.
Touraine
Touraine is a historic region in central France, famed for its Loire Valley châteaux, wine production, and role as a former royal heartland.
-
D.
Alsace
Alsace is a historical and cultural region in northeastern France known for its blend of French and German influences, picturesque villages, and renowned wines.
-
E.
Anjou
Anjou is a historic region in western France that was once a powerful medieval county and later a duchy, playing a central role in the Angevin Empire and European dynastic politics.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36cf4d8608190bf3d33ee6b93aae0 |
completed | Feb. 28, 2026, 10:32 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.