Triple
T21039098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guy Demers |
E518270
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Guy Demers |
—
|
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: Guy Demers | Statement: [Guy Demers, name, Guy Demers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guy Demers Context triple: [Guy Demers, name, Guy Demers]
-
A.
Guy Demers
chosen
Guy Demers is an individual notable enough to be specifically cited as a bearer of the surname Demers.
-
B.
Luc Demers
Luc Demers is a person notable enough to be specifically cited as a bearer of the surname Demers, though detailed public information about his life or achievements is not widely documented.
-
C.
Louis-Philippe Demers
Louis-Philippe Demers was a Canadian lawyer and politician who served as a Liberal member of the House of Commons in the early 20th century.
-
D.
Steve Bégin
Steve Bégin is a retired Canadian professional ice hockey forward best known for his gritty, hard-working play during an NHL career that included long stints with the Montreal Canadiens and Calgary Flames.
-
E.
Cédric Champeau
Cédric Champeau is a prominent software engineer best known for his major contributions to the Groovy programming language and its ecosystem.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcee13b08190a8b3372f6759cd1b |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:13 p.m.