Triple
T6743604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brioude |
E154150
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Gérard Roche
Gérard Roche is a French local politician known for serving as the mayor of the commune of Brioude in south-central France.
|
E688787
|
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: Gérard Roche | Statement: [Brioude, hasMayor, Gérard Roche]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gérard Roche Context triple: [Brioude, hasMayor, Gérard Roche]
-
A.
Michel Virlogeux
Michel Virlogeux is a renowned French structural engineer and bridge designer known for his work on major long-span bridges around the world.
-
B.
Frédéric Bricout
Frédéric Bricout is a French politician who serves as the mayor of the northern French city of Cambrai.
-
C.
Thierry Delaporte
Thierry Delaporte is a French business executive best known as the CEO and Managing Director of Indian IT services giant Wipro.
-
D.
Didier Gailhaguet
Didier Gailhaguet is a French figure skating coach and former president of the French Ice Sports Federation, known for his influential and often controversial role in international figure skating.
-
E.
Bernard Guillembet
Bernard Guillembet was a mountaineer known for making the first recorded ascent of Vignemale in the Pyrenees.
- 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: Gérard Roche Triple: [Brioude, hasMayor, Gérard Roche]
Generated description
Gérard Roche is a French local politician known for serving as the mayor of the commune of Brioude in south-central France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gérard Roche Target entity description: Gérard Roche is a French local politician known for serving as the mayor of the commune of Brioude in south-central France.
-
A.
Michel Virlogeux
Michel Virlogeux is a renowned French structural engineer and bridge designer known for his work on major long-span bridges around the world.
-
B.
Frédéric Bricout
Frédéric Bricout is a French politician who serves as the mayor of the northern French city of Cambrai.
-
C.
Thierry Delaporte
Thierry Delaporte is a French business executive best known as the CEO and Managing Director of Indian IT services giant Wipro.
-
D.
Didier Gailhaguet
Didier Gailhaguet is a French figure skating coach and former president of the French Ice Sports Federation, known for his influential and often controversial role in international figure skating.
-
E.
Bernard Guillembet
Bernard Guillembet was a mountaineer known for making the first recorded ascent of Vignemale in the Pyrenees.
- 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_69c6880d84d8819095d19de2295f26ac |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1b3b1448190a94b4b64f01af14a |
completed | March 27, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8e5664fb8819098ca2138e7c1e044 |
completed | March 29, 2026, 8:40 a.m. |
| NEDg | Description generation | batch_69c8e65d13b88190a85b4074122a09c9 |
completed | March 29, 2026, 8:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8e6b9d0e48190ae1739ca5b68ee3d |
completed | March 29, 2026, 8:45 a.m. |
Created at: March 27, 2026, 2:10 p.m.