Triple
T15499955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julie Le Brun |
E378925
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Le Brun |
E227737
|
NE FINISHED |
How this triple was built (2 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: Le Brun | Statement: [Julie Le Brun, familyName, Le Brun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Brun Context triple: [Julie Le Brun, familyName, Le Brun]
-
A.
Lebrun
chosen
Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
-
B.
La Frenais
La Frenais is a surname most notably associated with British television writer Ian La Frenais, known for co-creating several classic UK comedy series.
-
C.
Tanguy
Tanguy is a French surname most notably associated with Yves Tanguy, a prominent 20th-century Surrealist painter.
-
D.
Béraud
Béraud is a French surname most notably associated with the 19th-century painter Jean Béraud, renowned for his vivid depictions of Parisian life during the Belle Époque.
-
E.
Gagnière
Gagnière is a French surname, likely of regional origin, associated with individuals such as Mahoudeau.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcb4e8c81908e4ab463e3ae252b |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3667a53c81908be789f99e580265 |
completed | May 9, 2026, 1:28 p.m. |
Created at: April 10, 2026, 3:54 a.m.