StatisticsGen
E457343
StatisticsGen is a TensorFlow Extended component that computes descriptive statistics over input datasets to support data analysis and validation in machine learning pipelines.
All labels observed (1)
| Label | Occurrences |
|---|---|
| StatisticsGen canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4654865 — 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: StatisticsGen Context triple: [TensorFlow Extended, hasComponent, StatisticsGen]
-
A.
Stat.
Stat. is the standard legal citation abbreviation for the United States Statutes at Large, the official chronological compilation of all federal laws and resolutions enacted by the U.S. Congress.
-
B.
Science Resources Statistics, National Science Foundation
Science Resources Statistics at the National Science Foundation is a federal statistical agency responsible for collecting, analyzing, and disseminating data on the U.S. science and engineering enterprise.
-
C.
Statsbygg
Statsbygg is the Norwegian government's key agency responsible for planning, constructing, managing, and maintaining state-owned buildings and properties.
-
D.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
-
E.
Statistics
Statistics is a Julia standard library module that provides basic statistical functions such as means, variances, and related summary measures for numerical data.
- 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: StatisticsGen Target entity description: StatisticsGen is a TensorFlow Extended component that computes descriptive statistics over input datasets to support data analysis and validation in machine learning pipelines.
-
A.
Stat.
Stat. is the standard legal citation abbreviation for the United States Statutes at Large, the official chronological compilation of all federal laws and resolutions enacted by the U.S. Congress.
-
B.
Science Resources Statistics, National Science Foundation
Science Resources Statistics at the National Science Foundation is a federal statistical agency responsible for collecting, analyzing, and disseminating data on the U.S. science and engineering enterprise.
-
C.
Statsbygg
Statsbygg is the Norwegian government's key agency responsible for planning, constructing, managing, and maintaining state-owned buildings and properties.
-
D.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
-
E.
Statistics
Statistics is a Julia standard library module that provides basic statistical functions such as means, variances, and related summary measures for numerical data.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
TFX component
ⓘ
TensorFlow Extended component ⓘ |
| belongsTo |
data analysis stage of ML pipelines
ⓘ
data validation stage of ML pipelines ⓘ |
| canCompute |
feature histograms
ⓘ
feature means ⓘ feature standard deviations ⓘ feature value counts ⓘ missing value statistics ⓘ |
| compatibleWith |
Airflow
NERFINISHED
ⓘ
Apache Beam runners NERFINISHED ⓘ Kubeflow Pipelines NERFINISHED ⓘ Vertex AI Pipelines NERFINISHED ⓘ |
| configurableWith | StatsOptions ⓘ |
| developedBy | Google NERFINISHED ⓘ |
| documentationURL | https://www.tensorflow.org/tfx/guide/statsgen ⓘ |
| hasParameter |
example_gen
ⓘ
output ⓘ stats_options ⓘ |
| hasPurpose |
compute descriptive statistics over input datasets
ⓘ
support data analysis in machine learning pipelines ⓘ support data validation in machine learning pipelines ⓘ |
| implementedIn | Python NERFINISHED ⓘ |
| inputType |
ExampleGen output
ⓘ
tf.Example data ⓘ tf.Record files ⓘ |
| license | Apache License 2.0 ⓘ |
| outputFormat | protocol buffers ⓘ |
| outputType |
ExampleStatistics
ⓘ
statistics artifact ⓘ |
| outputUsedBy |
ExampleValidator
NERFINISHED
ⓘ
SchemaGen NERFINISHED ⓘ other downstream TFX components ⓘ |
| partOf |
TFX pipeline
NERFINISHED
ⓘ
TensorFlow Extended NERFINISHED ⓘ |
| repositoryURL | https://github.com/tensorflow/tfx ⓘ |
| runsIn | TFX orchestration environment ⓘ |
| supports |
feature-wise statistics computation
ⓘ
per-split statistics computation ⓘ slice-based statistics computation ⓘ |
| usedFor |
data validation
ⓘ
detecting data anomalies ⓘ exploratory data analysis ⓘ schema inference support ⓘ |
| usesLibrary |
TensorFlow Data Validation
NERFINISHED
ⓘ
tfdv NERFINISHED ⓘ |
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: StatisticsGen Description of subject: StatisticsGen is a TensorFlow Extended component that computes descriptive statistics over input datasets to support data analysis and validation in machine learning pipelines.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.