Triple
T16387125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurent Jouvenet |
E397951
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Jouvenet
Jouvenet is a French surname historically associated with several notable artists and craftsmen, particularly during the 17th and 18th centuries.
|
E1209820
|
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: Jouvenet | Statement: [Laurent Jouvenet, familyName, Jouvenet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jouvenet Context triple: [Laurent Jouvenet, familyName, Jouvenet]
-
A.
Cluzet
Cluzet is a French surname most notably borne by actor François Cluzet, known for his prominent roles in contemporary French cinema.
-
B.
Monnier
Monnier is a French surname borne by various notable figures in literature, arts, and public life.
-
C.
Jeanneret
Jeanneret is a Swiss surname most notably associated with architect Pierre Jeanneret, a key collaborator of Le Corbusier in modernist architecture.
-
D.
Darrouzett
Darrouzett is a small rural town located in the Texas Panhandle region of the United States.
-
E.
Guerin
Guerin is a surname of French origin borne by various notable individuals across fields such as arts, sports, and public life.
- 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: Jouvenet Triple: [Laurent Jouvenet, familyName, Jouvenet]
Generated description
Jouvenet is a French surname historically associated with several notable artists and craftsmen, particularly during the 17th and 18th centuries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jouvenet Target entity description: Jouvenet is a French surname historically associated with several notable artists and craftsmen, particularly during the 17th and 18th centuries.
-
A.
Cluzet
Cluzet is a French surname most notably borne by actor François Cluzet, known for his prominent roles in contemporary French cinema.
-
B.
Monnier
Monnier is a French surname borne by various notable figures in literature, arts, and public life.
-
C.
Jeanneret
Jeanneret is a Swiss surname most notably associated with architect Pierre Jeanneret, a key collaborator of Le Corbusier in modernist architecture.
-
D.
Darrouzett
Darrouzett is a small rural town located in the Texas Panhandle region of the United States.
-
E.
Guerin
Guerin is a surname of French origin borne by various notable individuals across fields such as arts, sports, and public life.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e3263e1534819081a6bf5006c611c5 |
completed | April 18, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356ed47c819085aaf101459dd55c |
completed | May 10, 2026, 7:36 a.m. |
| NEDg | Description generation | batch_6a00368287d48190b510541eb7851942 |
completed | May 10, 2026, 7:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a003766d4ec8190ab98387781f85bd6 |
completed | May 10, 2026, 7:44 a.m. |
Created at: April 10, 2026, 5:08 a.m.