Triple
T12188907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monthélie AOC |
E290408
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Monthélie |
E59846
|
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: Monthélie | Statement: [Monthélie AOC, namedAfter, Monthélie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monthélie Context triple: [Monthélie AOC, namedAfter, Monthélie]
-
A.
Monthélie
chosen
Monthélie is a small wine-producing village in Burgundy’s Côte de Beaune, known for its elegant red and white wines made primarily from Pinot Noir and Chardonnay.
-
B.
Mouriès
Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
-
C.
Marloie
Marloie is a village in the Walloon region of Belgium known for its railway station on the Brussels–Luxembourg line.
-
D.
Malaucène
Malaucène is a picturesque Provençal village in southeastern France, known as a popular base for cyclists and tourists visiting and climbing Mont Ventoux.
-
E.
Montgeron
Montgeron is a suburban commune in the southern outskirts of Paris, France, known for its residential character and location within the Essonne department in the Île-de-France region.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c5102848190ad652c3d6445f65a |
completed | April 10, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f6b0a84c8190ae593e368c13b5a5 |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:50 p.m.