Université Laval
E25841
Université Laval is a major French-language public research university in Quebec City, recognized for its strong research output and membership in Canada’s leading research-intensive university groups.
All labels observed (4)
| Label | Occurrences |
|---|---|
| Université Laval canonical | 35 |
| Laval University | 1 |
| Université Laval Faculty of Law | 1 |
| Université Laval campus | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T161828 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Université Laval Context triple: [U15 Group of Canadian Research Universities, member, Université Laval]
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A.
Université de Montréal
Université de Montréal is a major French-language public research university in Montreal, Canada, renowned for its contributions to fields such as artificial intelligence and deep learning.
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B.
Université du Québec à Montréal
Université du Québec à Montréal is a major French-language public university known for its urban campus, research activity, and strong presence in the cultural and academic life of Montreal.
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C.
McGill University
McGill University is a leading public research university in Montreal, Canada, renowned for its strong academic programs, global reputation, and membership in major research-intensive university associations.
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D.
Concordia University
Concordia University is a major public research university in Montreal, Canada, known for its diverse student body and strong programs in arts, engineering, business, and social sciences.
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E.
University of New Brunswick
The University of New Brunswick is a public research university in New Brunswick, Canada, recognized as one of the country’s oldest English-language universities and noted for its programs in engineering, computer science, and business.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Université Laval Target entity description: Université Laval is a major French-language public research university in Quebec City, recognized for its strong research output and membership in Canada’s leading research-intensive university groups.
-
A.
Université de Montréal
Université de Montréal is a major French-language public research university in Montreal, Canada, renowned for its contributions to fields such as artificial intelligence and deep learning.
-
B.
Université du Québec à Montréal
Université du Québec à Montréal is a major French-language public university known for its urban campus, research activity, and strong presence in the cultural and academic life of Montreal.
-
C.
McGill University
McGill University is a leading public research university in Montreal, Canada, renowned for its strong academic programs, global reputation, and membership in major research-intensive university associations.
-
D.
Concordia University
Concordia University is a major public research university in Montreal, Canada, known for its diverse student body and strong programs in arts, engineering, business, and social sciences.
-
E.
University of New Brunswick
The University of New Brunswick is a public research university in New Brunswick, Canada, recognized as one of the country’s oldest English-language universities and noted for its programs in engineering, computer science, and business.
- F. None of above. chosen
Statements (47)
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Université Laval Description of subject: Université Laval is a major French-language public research university in Quebec City, recognized for its strong research output and membership in Canada’s leading research-intensive university groups.
Referenced by (38)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Université Laval
subject linked to:
Quebec, Canada
linked to: Université Laval
linked to: Université Laval
subject linked to:
Quebec City (typical seat of Quebec ministries, exact address not confirmed)