Triple
T9444829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Le Brun |
E227737
|
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: [Charles Le Brun, familyName, Le Brun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Brun Context triple: [Charles 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.
Tanguy
Tanguy is a French surname most notably associated with Yves Tanguy, a prominent 20th-century Surrealist painter.
-
C.
Ganthier
Ganthier is a commune in western Haiti known for its rural character and proximity to the capital, Port-au-Prince.
-
D.
Chastre
Chastre is a municipality in central Belgium known for its rural character and location within the province of Walloon Brabant.
-
E.
Greuze
Greuze is a French surname most famously associated with Jean-Baptiste Greuze, an 18th-century painter known for his sentimental and moralizing genre scenes.
- 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_69ca843884488190ad6cbe0153088234 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f32aee88190a43573f97fa1e49d |
completed | April 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cd9ae548190bf985c72196eb0bd |
completed | April 4, 2026, 3:23 p.m. |
Created at: March 30, 2026, 7:51 p.m.