Triple
T17600991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leclerc |
E428698
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Michel Leclerc
Michel Leclerc is a French film director and screenwriter known for socially engaged comedies such as "Le Nom des gens" ("The Names of Love").
|
E1278904
|
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: Michel Leclerc | Statement: [Leclerc, hasNotableBearer, Michel Leclerc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michel Leclerc Context triple: [Leclerc, hasNotableBearer, Michel Leclerc]
-
A.
Jean-Noël Duclos
Jean-Noël Duclos is a French local politician who serves as the mayor of the commune of Survilliers in northern France.
-
B.
Maurice Forget
Maurice Forget is a sports official best known for delivering the judges' oath at the 1976 Summer Olympics in Montreal.
-
C.
Jean Ducos
Jean Ducos was a French politician who served as a deputy during the French Revolution.
-
D.
Jean-Paul Laurens
Jean-Paul Laurens was a prominent 19th-century French painter and sculptor known for his dramatic historical and religious scenes rendered in an academic style.
-
E.
Jean Leclerc
Jean Leclerc was a 17th-century French Protestant theologian and biblical scholar known for his critical and rationalist approach to theology and exegesis.
- 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: Michel Leclerc Triple: [Leclerc, hasNotableBearer, Michel Leclerc]
Generated description
Michel Leclerc is a French film director and screenwriter known for socially engaged comedies such as "Le Nom des gens" ("The Names of Love").
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michel Leclerc
Target entity description: Michel Leclerc is a French film director and screenwriter known for socially engaged comedies such as "Le Nom des gens" ("The Names of Love").
-
A.
Jean-Noël Duclos
Jean-Noël Duclos is a French local politician who serves as the mayor of the commune of Survilliers in northern France.
-
B.
Maurice Forget
Maurice Forget is a sports official best known for delivering the judges' oath at the 1976 Summer Olympics in Montreal.
-
C.
Jean Ducos
Jean Ducos was a French politician who served as a deputy during the French Revolution.
-
D.
Jean-Paul Laurens
Jean-Paul Laurens was a prominent 19th-century French painter and sculptor known for his dramatic historical and religious scenes rendered in an academic style.
-
E.
Jean Leclerc
Jean Leclerc was a 17th-century French Protestant theologian and biblical scholar known for his critical and rationalist approach to theology and exegesis.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c48dfc08190ba360e6082cffa87 |
completed | April 19, 2026, 5:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a020a8ed8d081908346127e09f39512 |
completed | May 11, 2026, 4:57 p.m. |
| NEDg | Description generation | batch_6a020bc148c88190b0a59366ed87012b |
completed | May 11, 2026, 5:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a020c2ee784819082c0bbf09c12813b |
completed | May 11, 2026, 5:04 p.m. |
Created at: April 10, 2026, 5:51 a.m.