Triple
T17498371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prudent Beaudry |
E426130
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Prudent Beaudry |
—
|
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: Prudent Beaudry | Statement: [Prudent Beaudry, name, Prudent Beaudry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prudent Beaudry Context triple: [Prudent Beaudry, name, Prudent Beaudry]
-
A.
Prudent Beaudry
chosen
Prudent Beaudry was a 19th-century Canadian-born businessman and politician who served as mayor of Los Angeles and played a key role in the city’s early development.
-
B.
Paul Gervais
Paul Gervais was a French painter known for his large-scale decorative works and allegorical murals in prominent public buildings.
-
C.
Jean Demers
Jean Demers is a person notable enough to be recognized as a significant bearer of the surname Demers.
-
D.
Philippe Beaudoin
Philippe Beaudoin is a Canadian computer scientist and entrepreneur known for co-founding the artificial intelligence company Element AI.
-
E.
Pierre Cossette
Pierre Cossette was a prominent Canadian-American television and theater producer best known for bringing the Grammy Awards to television and producing major Broadway and live entertainment productions.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4521028048190aa7c4023a72a12f4 |
completed | April 19, 2026, 3:54 a.m. |
Created at: April 10, 2026, 5:48 a.m.