Colander validation library
E552011
Colander validation library is a Python package for declaratively defining and validating data structures, often used for configuration and web form data.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Colander validation library canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T5838715 — 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: Colander validation library Context triple: [Pylons Project, hasComponent, Colander validation library]
-
A.
Validator
Validator is a Symfony component that provides a flexible validation system for checking and enforcing constraints on data and objects in PHP applications.
-
B.
ExampleValidator
ExampleValidator is a TensorFlow Extended component that automatically analyzes input data to detect anomalies and validate examples before they are used in machine learning pipelines.
-
C.
Jakarta Bean Validation
Jakarta Bean Validation is a Jakarta EE specification that defines a standard, annotation-based way to declare and enforce constraints on Java object models, typically used for validating user input and application data.
-
D.
InfraValidator
InfraValidator is a TensorFlow Extended component that validates the serving infrastructure and model deployment environment to ensure models can be safely and correctly served in production.
-
E.
Schematron
Schematron is a rule-based XML schema language that uses XPath expressions to define and validate complex structural and business constraints in XML documents.
- 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: Colander validation library Target entity description: Colander validation library is a Python package for declaratively defining and validating data structures, often used for configuration and web form data.
-
A.
Validator
Validator is a Symfony component that provides a flexible validation system for checking and enforcing constraints on data and objects in PHP applications.
-
B.
ExampleValidator
ExampleValidator is a TensorFlow Extended component that automatically analyzes input data to detect anomalies and validate examples before they are used in machine learning pipelines.
-
C.
Jakarta Bean Validation
Jakarta Bean Validation is a Jakarta EE specification that defines a standard, annotation-based way to declare and enforce constraints on Java object models, typically used for validating user input and application data.
-
D.
InfraValidator
InfraValidator is a TensorFlow Extended component that validates the serving infrastructure and model deployment environment to ensure models can be safely and correctly served in production.
-
E.
Schematron
Schematron is a rule-based XML schema language that uses XPath expressions to define and validate complex structural and business constraints in XML documents.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data validation library ⓘ |
| compatibleWith | Pyramid web framework NERFINISHED ⓘ |
| dataModel | tree of schema nodes ⓘ |
| distribution | Python Package Index NERFINISHED ⓘ |
| errorHandling | structured error objects ⓘ |
| hasConcept |
Length validator
ⓘ
Mapping ⓘ OneOf validator ⓘ Range validator ⓘ Regex validator ⓘ Schema ⓘ SchemaNode ⓘ Sequence ⓘ Tuple ⓘ |
| inputType | arbitrary Python data structures ⓘ |
| installCommand | pip install colander ⓘ |
| license | BSD-like license ⓘ |
| oftenUsedWith |
Deform form library
NERFINISHED
ⓘ
Pyramid ⓘ |
| outputType | basic Python types ⓘ |
| primaryUse |
data deserialization
ⓘ
data serialization ⓘ data validation ⓘ schema-based validation ⓘ |
| programmingLanguage | Python ⓘ |
| supports | Python 3 NERFINISHED ⓘ |
| supportsFeature |
custom validators
ⓘ
data coercion ⓘ declarative schemas ⓘ default values ⓘ deserialization from basic Python types ⓘ error collection ⓘ localization of error messages ⓘ mapping schemas ⓘ required and optional fields ⓘ schema nodes ⓘ sequence schemas ⓘ serialization to basic Python types ⓘ type-based validation ⓘ |
| supportsLanguage | Python ⓘ |
| typicalDomain |
REST APIs
NERFINISHED
ⓘ
configuration management ⓘ web applications ⓘ |
| usedFor |
defining data structures declaratively
ⓘ
validating JSON data ⓘ validating configuration data ⓘ validating nested data structures ⓘ validating web form data ⓘ |
| validationStyle | declarative ⓘ |
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 validation library Description of subject: Colander validation library is a Python package for declaratively defining and validating data structures, often used for configuration and web form data.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.