sns
E96639
sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
All labels observed (1)
| Label | Occurrences |
|---|---|
| sns canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T825590 — 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: sns Context triple: [Seaborn, typicalImportName, sns]
-
A.
SN
SN is the commonly used abbreviated form of the Ukrainian political party and former television series "Servant of the People."
-
B.
NS
NS is the standard abbreviation for Norfolk Southern Railway, a major Class I freight railroad operating primarily in the eastern United States.
-
C.
SEN
SEN is the three-letter IATA airport code for London Southend Airport in the United Kingdom.
-
D.
SAM
SAM is an analytical laboratory aboard NASA's Curiosity rover that studies Martian rocks, soil, and atmosphere to determine their chemical and organic composition.
-
E.
SO
SO is the New York Stock Exchange ticker symbol for Southern Company, a major U.S. electric and gas utility holding company based in the Southeast.
- 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: sns Target entity description: sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
-
A.
SN
SN is the commonly used abbreviated form of the Ukrainian political party and former television series "Servant of the People."
-
B.
NS
NS is the standard abbreviation for Norfolk Southern Railway, a major Class I freight railroad operating primarily in the eastern United States.
-
C.
SEN
SEN is the three-letter IATA airport code for London Southend Airport in the United Kingdom.
-
D.
SAM
SAM is an analytical laboratory aboard NASA's Curiosity rover that studies Martian rocks, soil, and atmosphere to determine their chemical and organic composition.
-
E.
SO
SO is the New York Stock Exchange ticker symbol for Southern Company, a major U.S. electric and gas utility holding company based in the Southeast.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf | Python library alias ⓘ |
| abbreviationOf | seaborn namespace in Python code ⓘ |
| aliasFor |
seaborn
ⓘ
linked to:
Seaborn
|
| associatedWith |
data visualization
ⓘ
statistical graphics ⓘ |
| builtOnTopOf | Matplotlib ⓘ |
| category |
data visualization tool
ⓘ
statistical plotting interface ⓘ |
| commonlyUsedWith |
NumPy
ⓘ
pandas ⓘ |
| domain |
data analysis
ⓘ
machine learning ⓘ scientific computing ⓘ |
| ecosystem | Python scientific stack ⓘ |
| providesAccessTo |
seaborn.barplot
ⓘ
seaborn.boxplot ⓘ
linked to:
Seaborn
seaborn.catplot ⓘ seaborn.clustermap ⓘ
linked to:
Seaborn
seaborn.countplot ⓘ seaborn.displot ⓘ seaborn.heatmap ⓘ seaborn.histplot ⓘ seaborn.jointplot ⓘ seaborn.kdeplot ⓘ seaborn.lineplot ⓘ seaborn.lmplot ⓘ seaborn.pairplot ⓘ seaborn.relplot ⓘ seaborn.scatterplot ⓘ seaborn.set_context ⓘ seaborn.set_style ⓘ seaborn.set_theme ⓘ seaborn.violinplot ⓘ
linked to:
Seaborn
|
| relatedTo |
Matplotlib
ⓘ
NumPy ⓘ pandas ⓘ |
| typicalCompanionImport |
import matplotlib.pyplot as plt
ⓘ
import pandas as pd ⓘ |
| typicalImportStatement | import seaborn as sns ⓘ |
| usedFor |
creating statistical plots
ⓘ
quick exploratory data analysis ⓘ visualizing categorical data ⓘ visualizing correlation matrices ⓘ visualizing distributions ⓘ visualizing relationships between variables ⓘ |
| usedInLanguage | Python ⓘ |
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: sns Description of subject: sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.