Triple
T18940373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bové |
E463363
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | Joseph Bové |
—
|
NE NERFINISHED |
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: Joseph Bové | Statement: [Bové, usedBy, Joseph Bové]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph Bové Context triple: [Bové, usedBy, Joseph Bové]
-
A.
Joseph Bové
chosen
Joseph Bové was a prominent 19th-century Russian neoclassical architect known for helping reshape central Moscow’s cityscape after the 1812 fire.
-
B.
Michel Lefait
Michel Lefait is a French politician known for his role in establishing the centrist political party Union des Démocrates et Indépendants (UDI).
-
C.
Noël Quillerier
Noël Quillerier was a 17th-century French painter and art teacher active in Paris, known for training artists such as Noël Coypel.
-
D.
Jean-Marie Bonnassieux
Jean-Marie Bonnassieux was a 19th-century French sculptor known for his religious and monumental works in stone and bronze.
-
E.
Paul-André Meyer
Paul-André Meyer was a French mathematician renowned for his foundational contributions to probability theory and stochastic processes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d3eae9b88190b6031c359090782a |
completed | April 20, 2026, 7:21 a.m. |
Created at: April 10, 2026, 11:59 a.m.