Triple
T21379461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grigny |
E527303
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Philippe Rio
Philippe Rio is a French politician known for serving as the long-time communist mayor of the disadvantaged Paris suburb of Grigny and for his work on social cohesion and urban policy.
|
E1491807
|
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: Philippe Rio | Statement: [Grigny, hasMayor, Philippe Rio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Philippe Rio Context triple: [Grigny, hasMayor, Philippe Rio]
-
A.
Philippe Denis
Philippe Denis is a cinematographer best known for his work on the animated film "Megamind."
-
B.
Philippe Martin
Philippe Martin is a French film producer known for his work on acclaimed European cinema.
-
C.
Philippe Martin
Philippe Martin is the central protagonist of the romantic drama film "When Tomorrow Comes."
-
D.
Philippe Ledormeur
Philippe Ledormeur was a mountaineer known for participating in the pioneering first ascent of Peru’s Huascarán, one of the highest peaks in the Andes.
-
E.
Christophe Pélissier
Christophe Pélissier is a French football coach known for guiding modest clubs to promotion in the French league system.
- 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: Philippe Rio Triple: [Grigny, hasMayor, Philippe Rio]
Generated description
Philippe Rio is a French politician known for serving as the long-time communist mayor of the disadvantaged Paris suburb of Grigny and for his work on social cohesion and urban policy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Philippe Rio Target entity description: Philippe Rio is a French politician known for serving as the long-time communist mayor of the disadvantaged Paris suburb of Grigny and for his work on social cohesion and urban policy.
-
A.
Philippe Denis
Philippe Denis is a cinematographer best known for his work on the animated film "Megamind."
-
B.
Philippe Martin
Philippe Martin is a French film producer known for his work on acclaimed European cinema.
-
C.
Philippe Martin
Philippe Martin is the central protagonist of the romantic drama film "When Tomorrow Comes."
-
D.
Philippe Ledormeur
Philippe Ledormeur was a mountaineer known for participating in the pioneering first ascent of Peru’s Huascarán, one of the highest peaks in the Andes.
-
E.
Christophe Pélissier
Christophe Pélissier is a French football coach known for guiding modest clubs to promotion in the French league system.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0cc2b5c8190aa5f20f920523fe9 |
completed | April 22, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09f65d716c81908ccf2f8732892e33 |
completed | May 17, 2026, 5:09 p.m. |
| NEDg | Description generation | batch_6a09f78a3d788190b4b19f3b6463550e |
completed | May 17, 2026, 5:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09f825b02c819096f57b1dba02ded2 |
completed | May 17, 2026, 5:17 p.m. |
Created at: April 16, 2026, 5:11 p.m.