Triple
T13856301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morangis |
E333072
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Olivier Corzani
Olivier Corzani is a French local politician who serves as the mayor of the commune of Morangis.
|
E1077811
|
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: Olivier Corzani | Statement: [Morangis, hasMayor, Olivier Corzani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Olivier Corzani Context triple: [Morangis, hasMayor, Olivier Corzani]
-
A.
Olivier Bousquet
Olivier Bousquet is a French computer scientist and machine learning researcher known for his work on statistical learning theory and his leadership roles in industry AI research.
-
B.
Arnaud Decagny
Arnaud Decagny is a French local politician who serves as the mayor of the northern town of Maubeuge.
-
C.
Arnaud Vaillant
Arnaud Vaillant is a French fashion designer and co-founder of the innovative Paris-based label Coperni, known for its tech-inspired, sculptural womenswear.
-
D.
Olivier Cresp
Olivier Cresp is a renowned French master perfumer, best known for creating iconic fragrances for major luxury brands.
-
E.
Arnaud Péricard
Arnaud Péricard is a French politician and lawyer known for serving as the mayor of the affluent Parisian suburb of Saint-Germain-en-Laye.
- 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: Olivier Corzani Triple: [Morangis, hasMayor, Olivier Corzani]
Generated description
Olivier Corzani is a French local politician who serves as the mayor of the commune of Morangis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Olivier Corzani Target entity description: Olivier Corzani is a French local politician who serves as the mayor of the commune of Morangis.
-
A.
Olivier Bousquet
Olivier Bousquet is a French computer scientist and machine learning researcher known for his work on statistical learning theory and his leadership roles in industry AI research.
-
B.
Arnaud Decagny
Arnaud Decagny is a French local politician who serves as the mayor of the northern town of Maubeuge.
-
C.
Arnaud Vaillant
Arnaud Vaillant is a French fashion designer and co-founder of the innovative Paris-based label Coperni, known for its tech-inspired, sculptural womenswear.
-
D.
Olivier Cresp
Olivier Cresp is a renowned French master perfumer, best known for creating iconic fragrances for major luxury brands.
-
E.
Arnaud Péricard
Arnaud Péricard is a French politician and lawyer known for serving as the mayor of the affluent Parisian suburb of Saint-Germain-en-Laye.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02dc9f488190b7181dcb7e304632 |
completed | April 14, 2026, 9:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb648146c8190842a3da4e4c0e217 |
completed | May 7, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_69fcc51bf140819097bb29bbaf766dcc |
completed | May 7, 2026, 5 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcc61353d481908192a1e2f44e6a94 |
completed | May 7, 2026, 5:04 p.m. |
Created at: April 9, 2026, 10:14 p.m.