Triple
T1464059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nièvre |
E31578
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Nevers
Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
|
E172114
|
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: Nevers | Statement: [Nièvre, capital, Nevers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nevers Context triple: [Nièvre, capital, Nevers]
-
A.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
B.
Roanne
Roanne is a commune and industrial town in central France, situated on the Loire River and known historically for its textile industry and river port.
-
C.
Choulex
Choulex is a small municipality in the canton of Geneva in southwestern Switzerland, known for its rural character and proximity to the city of Geneva.
-
D.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
E.
Dijon
Dijon is a historic city in eastern France renowned for its rich architectural heritage, former status as the capital of the Duchy of Burgundy, and its famous mustard.
- 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: Nevers Triple: [Nièvre, capital, Nevers]
Generated description
Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nevers Target entity description: Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
-
A.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
B.
Roanne
Roanne is a commune and industrial town in central France, situated on the Loire River and known historically for its textile industry and river port.
-
C.
Choulex
Choulex is a small municipality in the canton of Geneva in southwestern Switzerland, known for its rural character and proximity to the city of Geneva.
-
D.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
E.
Dijon
Dijon is a historic city in eastern France renowned for its rich architectural heritage, former status as the capital of the Duchy of Burgundy, and its famous mustard.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b89708819084fb9ba4ff293b8b |
completed | March 1, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad232ab26c8190aa9fc95ff2fcf0eb |
completed | March 8, 2026, 7:20 a.m. |
| NEDg | Description generation | batch_69ad23b8d570819099b953c7a60e9445 |
completed | March 8, 2026, 7:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad248193ec8190b0c08ef979661af0 |
completed | March 8, 2026, 7:25 a.m. |
Created at: March 1, 2026, 8 p.m.