In School
E123242
In School is a non-fiction book by former NHL goaltender and Canadian politician Ken Dryden that examines the challenges and possibilities of the modern education system.
All labels observed (1)
| Label | Occurrences |
|---|---|
| In School canonical | 2 |
Statements (17)
| Predicate | Object |
|---|---|
| instanceOf | non-fiction book ⓘ |
| author | Ken Dryden ⓘ |
| authorName | Ken Dryden ⓘ |
| authorOccupation |
Canadian politician
ⓘ
former NHL goaltender ⓘ |
| countryOfOrigin | Canada ⓘ |
| describes |
challenges of the modern education system
ⓘ
possibilities of the modern education system ⓘ |
| genre |
education
ⓘ
non-fiction ⓘ |
| language | English ⓘ |
| mainSubject |
education system
ⓘ
educational reform ⓘ learning ⓘ schools ⓘ teaching ⓘ |
| mediaType | print ⓘ |
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: In School Description of subject: In School is a non-fiction book by former NHL goaltender and Canadian politician Ken Dryden that examines the challenges and possibilities of the modern education system.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.