ExampleValidator
E457345
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| ExampleValidator canonical | 5 |
How this entity was disambiguated
This entity first appeared as the object of triple T4654867 — 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: ExampleValidator Context triple: [TensorFlow Extended, hasComponent, ExampleValidator]
-
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.
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.
-
C.
W3C markup validation tools
W3C markup validation tools are online and software-based services provided by the World Wide Web Consortium that check web documents for compliance with HTML, XHTML, and other web standards.
-
D.
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.
-
E.
Verification Annex
The Verification Annex is a key component of the Chemical Weapons Convention that sets out detailed procedures and requirements for monitoring, inspecting, and verifying compliance with the treaty’s prohibitions on chemical weapons.
- 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: ExampleValidator Target entity description: 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.
-
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.
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.
-
C.
W3C markup validation tools
W3C markup validation tools are online and software-based services provided by the World Wide Web Consortium that check web documents for compliance with HTML, XHTML, and other web standards.
-
D.
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.
-
E.
Verification Annex
The Verification Annex is a key component of the Chemical Weapons Convention that sets out detailed procedures and requirements for monitoring, inspecting, and verifying compliance with the treaty’s prohibitions on chemical weapons.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
TensorFlow Extended component
ⓘ
data validation tool ⓘ machine learning pipeline component ⓘ |
| canBeRun |
as part of a TFX pipeline
ⓘ
in orchestrators such as Apache Airflow ⓘ in orchestrators such as Kubeflow Pipelines ⓘ |
| canDetect |
distributional drift
ⓘ
missing feature values ⓘ out-of-range feature values ⓘ schema violations ⓘ unexpected feature types ⓘ |
| category |
MLOps tooling
ⓘ
data quality ⓘ |
| configuration | ExampleValidatorConfig ⓘ |
| dependsOn |
TensorFlow Data Validation
ⓘ
linked to:
TF Data Validation
|
| developedBy | Google ⓘ |
| documentationURL | https://www.tensorflow.org/tfx/guide/exampleval ⓘ |
| hasGoal |
improve reliability of ML pipelines
ⓘ
prevent bad data from entering ML models ⓘ |
| implementedIn | Python ⓘ |
| input |
TFX Example artifacts
ⓘ
data schema ⓘ data statistics ⓘ |
| integratesWith |
Evaluator
ⓘ
ExampleGen ⓘ StatisticsGen ⓘ Trainer ⓘ |
| operatesOn |
evaluation data
ⓘ
serving data ⓘ training data ⓘ |
| output |
TFX ExampleValidation artifacts
ⓘ
anomalies report ⓘ validation results ⓘ |
| partOf | TensorFlow Extended ⓘ |
| relatedTo |
TensorFlow Data Validation
ⓘ
linked to:
TF Data Validation
TensorFlow Extended ⓘ |
| supports |
batch data validation
ⓘ
validation in production pipelines ⓘ |
| usedFor |
automatic analysis of input data
ⓘ
detecting data anomalies ⓘ ensuring data quality before model training ⓘ identifying training-serving skew ⓘ schema-based data validation ⓘ statistical data validation ⓘ validating examples ⓘ |
| usedIn |
TensorFlow Extended pipelines
ⓘ
linked to:
TensorFlow Extended
machine learning pipelines ⓘ |
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: ExampleValidator Description of subject: 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.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.