Triple
T13969128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pagé |
E336005
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Yves Pagé
Yves Pagé is a Canadian politician from Quebec who has served as a member of the National Assembly.
|
E1099142
|
NE FINISHED |
How this triple was built (4 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: Yves Pagé | Statement: [Pagé, hasNotableBearer, Yves Pagé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yves Pagé Context triple: [Pagé, hasNotableBearer, Yves Pagé]
-
A.
Yves Langlois
Yves Langlois is a film editor known for his work on the Academy Award–winning documentary short "The Lady in Number 6: Music Saved My Life."
-
B.
Yves Aucoin
Yves Aucoin is a renowned Canadian lighting designer best known for his long-term creative collaborations on major concert productions, particularly with Celine Dion.
-
C.
Alain Madelin
Alain Madelin is a French liberal-conservative politician and former minister known for his strong advocacy of free-market economic policies.
-
D.
Pierre Pagé
Pierre Pagé is a Canadian ice hockey coach and executive known for his roles behind the bench and in management in the NHL and European leagues.
-
E.
Alain Dostie
Alain Dostie is a Canadian cinematographer known for his visually rich work on numerous feature films, including collaborations with acclaimed directors such as Denys Arcand.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yves Pagé Triple: [Pagé, hasNotableBearer, Yves Pagé]
Generated description
Yves Pagé is a Canadian politician from Quebec who has served as a member of the National Assembly.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yves Pagé Target entity description: Yves Pagé is a Canadian politician from Quebec who has served as a member of the National Assembly.
-
A.
Yves Langlois
Yves Langlois is a film editor known for his work on the Academy Award–winning documentary short "The Lady in Number 6: Music Saved My Life."
-
B.
Yves Aucoin
Yves Aucoin is a renowned Canadian lighting designer best known for his long-term creative collaborations on major concert productions, particularly with Celine Dion.
-
C.
Alain Madelin
Alain Madelin is a French liberal-conservative politician and former minister known for his strong advocacy of free-market economic policies.
-
D.
Pierre Pagé
Pierre Pagé is a Canadian ice hockey coach and executive known for his roles behind the bench and in management in the NHL and European leagues.
-
E.
Alain Dostie
Alain Dostie is a Canadian cinematographer known for his visually rich work on numerous feature films, including collaborations with acclaimed directors such as Denys Arcand.
- F. None of above. chosen
Provenance (5 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bb227b48190bed1d19b8066b283 |
completed | May 8, 2026, 3:42 a.m. |
| NEDg | Description generation | batch_69fd5d585cc08190908bc5f9b8abdb82 |
completed | May 8, 2026, 3:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd5e0bbd6c8190b14039b3335692c7 |
completed | May 8, 2026, 3:52 a.m. |
Created at: April 9, 2026, 10:18 p.m.