Triple
T11886725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fuissé |
E282802
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Vergisson
Vergisson is a small wine-producing village in the Mâconnais region of Burgundy, France, known for its limestone cliffs and Pouilly-Fuissé vineyards.
|
E966867
|
NE FINISHED |
How this triple was built (4 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: Vergisson | Statement: [Fuissé, locatedNear, Vergisson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vergisson Context triple: [Fuissé, locatedNear, Vergisson]
-
A.
Ferrière
Ferrière is a French-language surname of Swiss origin borne by various notable individuals, including social worker and humanitarian Suzanne Ferrière.
-
B.
Vouziers
Vouziers is a small commune in northeastern France known for its historical role in World War I and its location in the rural Ardennes region.
-
C.
Brière
Brière is a French-language surname most prominently associated with former NHL player and current hockey executive Daniel Brière.
-
D.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
E.
Bourgueil
Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Vergisson Triple: [Fuissé, locatedNear, Vergisson]
Generated description
Vergisson is a small wine-producing village in the Mâconnais region of Burgundy, France, known for its limestone cliffs and Pouilly-Fuissé vineyards.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vergisson Target entity description: Vergisson is a small wine-producing village in the Mâconnais region of Burgundy, France, known for its limestone cliffs and Pouilly-Fuissé vineyards.
-
A.
Ferrière
Ferrière is a French-language surname of Swiss origin borne by various notable individuals, including social worker and humanitarian Suzanne Ferrière.
-
B.
Vouziers
Vouziers is a small commune in northeastern France known for its historical role in World War I and its location in the rural Ardennes region.
-
C.
Brière
Brière is a French-language surname most prominently associated with former NHL player and current hockey executive Daniel Brière.
-
D.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
E.
Bourgueil
Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
- F. None of above. chosen
Provenance (5 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_69d6ab2a90b08190a4e818821cc93e6d |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d3a13370819086386fecb99e4f0b |
completed | April 10, 2026, 10:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f6389ba08190b07fac8e90da0f5b |
completed | May 2, 2026, 1:03 p.m. |
| NEDg | Description generation | batch_69f601e0777081909e1212436680a10d |
completed | May 2, 2026, 1:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f602a21f948190849839301f49d55a |
completed | May 2, 2026, 1:56 p.m. |
Created at: April 8, 2026, 9:44 p.m.