Colander
E552007
Colander is a Python library used for data validation and deserialization, often employed in web applications to ensure structured and reliable input handling.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Colander canonical | 2 |
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data validation library ⓘ deserialization library ⓘ |
| category |
Python data validation tool
ⓘ
Python web development tool ⓘ |
| commonlyUsedWith |
Pyramid web framework
NERFINISHED
ⓘ
web applications ⓘ |
| designGoal |
clear error reporting
ⓘ
reliable input handling ⓘ structured data validation ⓘ |
| documentationFormat | Sphinx-based documentation ⓘ |
| hasConcept |
Deserialization
ⓘ
Node ⓘ Schema ⓘ Serialization ⓘ Validator ⓘ |
| hasFeature |
choice validation
ⓘ
custom type definitions ⓘ localization of error messages ⓘ mapping schemas ⓘ range validation ⓘ regular expression validation ⓘ schema nodes for different data types ⓘ sequence schemas ⓘ string length validation ⓘ |
| license | BSD-like license ⓘ |
| outputFormat | Python native data structures ⓘ |
| programmingLanguage | Python ⓘ |
| repositoryPlatform | GitHub NERFINISHED ⓘ |
| supports |
custom validators
ⓘ
data coercion ⓘ data deserialization ⓘ data serialization ⓘ declarative schema definitions ⓘ default values ⓘ error reporting ⓘ nested schemas ⓘ required and optional fields ⓘ type conversion ⓘ |
| typicalInputFormat |
JSON-like structures
ⓘ
Python dictionaries ⓘ |
| usedFor |
data deserialization
ⓘ
data validation ⓘ form and JSON validation ⓘ input validation in web applications ⓘ schema-based validation ⓘ structured input handling ⓘ |
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: Colander Description of subject: Colander is a Python library used for data validation and deserialization, often employed in web applications to ensure structured and reliable input handling.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.