Triple
T16246040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinsault |
E394373
|
entity |
| Predicate | hasSynonym |
P3575
|
FINISHED |
| Object | Black Malvoisie |
E971072
|
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: Black Malvoisie | Statement: [Cinsault, hasSynonym, Black Malvoisie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Black Malvoisie Context triple: [Cinsault, hasSynonym, Black Malvoisie]
-
A.
Leo le Gris
Leo le Gris is a pseudonym of León de Greiff, the influential 20th-century Colombian poet known for his erudite, musical, and highly stylized verse.
-
B.
Mistinguett
Mistinguett was a famous French actress and singer of the early 20th century, celebrated as one of Paris’s most iconic music-hall stars.
-
C.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
-
D.
Grolleau Noir
chosen
Grolleau Noir is a French red wine grape variety primarily used in the Loire Valley to produce light, fruity wines and rosés.
-
E.
Vacheresse
Vacheresse is a small mountain village in the French Alps, known for its traditional Savoyard character and location near the Abondance valley’s ski and hiking areas.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24561d250819096f709ea8751fcb9 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ee135b881909cc1b6919bc7af29 |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.