Triple
T7125948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fontenay-aux-Roses |
E166061
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Laurent Vastel
Laurent Vastel is a French local politician who serves as the mayor of the Paris suburb Fontenay-aux-Roses.
|
E689673
|
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: Laurent Vastel | Statement: [Fontenay-aux-Roses, hasMayor, Laurent Vastel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laurent Vastel Context triple: [Fontenay-aux-Roses, hasMayor, Laurent Vastel]
-
A.
Laurent Lomet
Laurent Lomet was a mountaineer known for participating in the first recorded ascent of Monte Perdido in the Pyrenees.
-
B.
Nicolas Dufourcq
Nicolas Dufourcq is a French business executive known for leading major technology and finance institutions, including serving as chairman of semiconductor company STMicroelectronics.
-
C.
Laurent Landi
Laurent Landi is an elite gymnastics coach best known for coaching Olympic champion Simone Biles.
-
D.
Laurent Barès
Laurent Barès is a French cinematographer known for his work on genre films, particularly in horror and action.
-
E.
Laurent Mauvignier
Laurent Mauvignier is a contemporary French novelist known for his psychologically intense, formally innovative works that often explore trauma, memory, and social marginalization.
- 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: Laurent Vastel Triple: [Fontenay-aux-Roses, hasMayor, Laurent Vastel]
Generated description
Laurent Vastel is a French local politician who serves as the mayor of the Paris suburb Fontenay-aux-Roses.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laurent Vastel Target entity description: Laurent Vastel is a French local politician who serves as the mayor of the Paris suburb Fontenay-aux-Roses.
-
A.
Laurent Lomet
Laurent Lomet was a mountaineer known for participating in the first recorded ascent of Monte Perdido in the Pyrenees.
-
B.
Nicolas Dufourcq
Nicolas Dufourcq is a French business executive known for leading major technology and finance institutions, including serving as chairman of semiconductor company STMicroelectronics.
-
C.
Laurent Landi
Laurent Landi is an elite gymnastics coach best known for coaching Olympic champion Simone Biles.
-
D.
Laurent Barès
Laurent Barès is a French cinematographer known for his work on genre films, particularly in horror and action.
-
E.
Laurent Mauvignier
Laurent Mauvignier is a contemporary French novelist known for his psychologically intense, formally innovative works that often explore trauma, memory, and social marginalization.
- 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e64d99888190a93c1822e19b5457 |
completed | March 27, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c902a459f481908dbba16de611b85a |
completed | March 29, 2026, 10:44 a.m. |
| NEDg | Description generation | batch_69c904629c18819085cc64d751780947 |
completed | March 29, 2026, 10:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c904c50e2c819084d43242ae8c10e2 |
completed | March 29, 2026, 10:53 a.m. |
Created at: March 27, 2026, 2:44 p.m.