Seaborn
E17844
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
All labels observed (8)
| Label | Occurrences |
|---|---|
| Seaborn canonical | 3 |
| FacetGrid | 1 |
| Seaborn library | 1 |
| seaborn | 1 |
| seaborn.boxplot | 1 |
| seaborn.clustermap | 1 |
| seaborn.pairplot | 1 |
| seaborn.violinplot | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T148137 — 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: Seaborn Context triple: [Python, visualizationLibrary, Seaborn]
-
A.
Matplotlib
Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
-
B.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
C.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
D.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
-
E.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
- 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: Seaborn Target entity description: Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
-
A.
Matplotlib
Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
-
B.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
C.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
D.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
-
E.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
- F. None of above. chosen
Statements (57)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data visualization library ⓘ statistical graphics library ⓘ |
| builtOnTopOf | Matplotlib ⓘ |
| designedFor | statistical analysis visualization ⓘ |
| hasAPI |
categorical plots API
ⓘ
distribution plots API ⓘ relational plots API ⓘ |
| hasFunction |
Seaborn
self-linksurface differs
ⓘ
surface form:
FacetGrid
JointGrid ⓘ PairGrid ⓘ barplot ⓘ boxplot ⓘ catplot ⓘ clustermap ⓘ countplot ⓘ displot ⓘ heatmap ⓘ jointplot ⓘ lmplot ⓘ pairplot ⓘ regplot ⓘ relplot ⓘ stripplot ⓘ swarmplot ⓘ violinplot ⓘ |
| integratesWith |
pandas
ⓘ
surface form:
Pandas
|
| license | BSD license ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
color palettes
ⓘ
default themes for Matplotlib ⓘ high-level interface for statistical graphics ⓘ tools for visualizing categorical data ⓘ tools for visualizing distributions ⓘ tools for visualizing linear relationships ⓘ |
| repositoryPlatform | GitHub ⓘ |
| specializesIn | statistical data visualization ⓘ |
| supports |
bar plots
ⓘ
box plots ⓘ categorical plots ⓘ confidence intervals ⓘ distribution plots ⓘ facet grids ⓘ heatmaps ⓘ histograms ⓘ joint plots ⓘ kernel density plots ⓘ line plots ⓘ mapping data to visual semantics ⓘ pair plots ⓘ regression plots ⓘ scatter plots ⓘ statistical estimation in plots ⓘ time series plots ⓘ violin plots ⓘ |
| typicalImportName | sns ⓘ |
| uses | Matplotlib axes objects ⓘ |
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: Seaborn Description of subject: Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
Referenced by (10)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
FacetGrid
this entity surface form:
seaborn.boxplot
this entity surface form:
seaborn.violinplot
this entity surface form:
seaborn.clustermap