Triple
T16173290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Estagel |
E392495
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Roger Ferrer
Roger Ferrer is a French local politician who serves as the mayor of the commune of Estagel in southern France.
|
E1198952
|
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: Roger Ferrer | Statement: [Estagel, hasMayor, Roger Ferrer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roger Ferrer Context triple: [Estagel, hasMayor, Roger Ferrer]
-
A.
Frank Ferrer
Frank Ferrer is an American rock drummer best known as the longtime drummer for the band Guns N' Roses.
-
B.
Eduardo Cansino
Eduardo Cansino was a Spanish-born dancer and choreographer best known as the father and early dance partner of Hollywood star Rita Hayworth.
-
C.
Larry Franco
Larry Franco is an American film producer known for his work on major Hollywood movies, including action, science fiction, and comic book adaptations.
-
D.
John Raffo
John Raffo is an American screenwriter best known for writing the biographical martial arts film "Dragon: The Bruce Lee Story."
-
E.
Edward Saenz
Edward Saenz is an artist and designer best known for creating the iconic Screen Actors Guild (SAG) Awards statuette.
- 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: Roger Ferrer Triple: [Estagel, hasMayor, Roger Ferrer]
Generated description
Roger Ferrer is a French local politician who serves as the mayor of the commune of Estagel in southern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Roger Ferrer Target entity description: Roger Ferrer is a French local politician who serves as the mayor of the commune of Estagel in southern France.
-
A.
Frank Ferrer
Frank Ferrer is an American rock drummer best known as the longtime drummer for the band Guns N' Roses.
-
B.
Eduardo Cansino
Eduardo Cansino was a Spanish-born dancer and choreographer best known as the father and early dance partner of Hollywood star Rita Hayworth.
-
C.
Larry Franco
Larry Franco is an American film producer known for his work on major Hollywood movies, including action, science fiction, and comic book adaptations.
-
D.
John Raffo
John Raffo is an American screenwriter best known for writing the biographical martial arts film "Dragon: The Bruce Lee Story."
-
E.
Edward Saenz
Edward Saenz is an artist and designer best known for creating the iconic Screen Actors Guild (SAG) Awards statuette.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb9b8208190b60874cec7a3a98e |
completed | April 17, 2026, 11:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fffefc3e088190975ecbdaeba7ee84 |
completed | May 10, 2026, 3:43 a.m. |
| NEDg | Description generation | batch_6a000086586c8190b5d93740f5b452a0 |
completed | May 10, 2026, 3:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0000e385bc819080e63ced564fe77b |
completed | May 10, 2026, 3:52 a.m. |
Created at: April 10, 2026, 5:02 a.m.