Triple
T8337594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trois-Rivières |
E195826
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object | Laviolette |
E660250
|
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: Laviolette | Statement: [Trois-Rivières, founder, Laviolette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laviolette Context triple: [Trois-Rivières, founder, Laviolette]
-
A.
Laviolette
chosen
Laviolette was a 17th-century French military officer and colonial administrator best known for establishing the settlement that became the city of Trois-Rivières in New France.
-
B.
Lovellette
Lovellette is the surname of Clyde Lovellette, a Hall of Fame American basketball player known for his collegiate success at Kansas and NBA career in the 1950s and 1960s.
-
C.
Oudry
Oudry is the surname of Jean-Baptiste Oudry, an 18th-century French painter and engraver renowned for his animal and hunting scenes.
-
D.
Crevant-Laveine
Crevant-Laveine is a small commune in central France’s Puy-de-Dôme department, characterized by its rural setting and traditional Auvergne landscape.
-
E.
Vallière
Vallière is a river in eastern France that flows through the town of Lons-le-Saunier in the Jura department.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fd5027c81909724f25aa30bbe58 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd95e4382081908634f31eb115e557 |
completed | April 1, 2026, 10:02 p.m. |
Created at: March 30, 2026, 5:57 p.m.