Dask
E426661
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Dask canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T4276920 — 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: Dask Context triple: [CuPy, integratesWith, Dask]
-
A.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
B.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
C.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
D.
Apache Flink
Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
-
E.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
- 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: Dask Target entity description: Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
A.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
B.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
C.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
D.
Apache Flink
Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
-
E.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
- F. None of above. chosen
Statements (59)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data processing framework ⓘ open-source software ⓘ parallel computing library ⓘ |
| canRunOn |
cloud infrastructure
ⓘ
cluster ⓘ multi-core machine ⓘ single machine ⓘ |
| compatibleWith |
NumPy
NERFINISHED
ⓘ
pandas ⓘ scikit-learn NERFINISHED ⓘ |
| developedIn | Python ecosystem ⓘ |
| hasComponent |
Dask Array
NERFINISHED
ⓘ
Dask Bag NERFINISHED ⓘ Dask DataFrame NERFINISHED ⓘ Dask Delayed NERFINISHED ⓘ Dask Distributed Scheduler NERFINISHED ⓘ Dask Futures NERFINISHED ⓘ Dask Local Scheduler NERFINISHED ⓘ |
| hasScheduler |
distributed scheduler
ⓘ
multi-process scheduler ⓘ multi-threaded scheduler ⓘ single-threaded scheduler ⓘ |
| isFreeSoftware | true ⓘ |
| license | BSD 3-Clause License NERFINISHED ⓘ |
| primaryUse |
large-scale data processing
ⓘ
parallelizing Python code ⓘ scaling single-machine workflows to clusters ⓘ |
| programmingLanguage | Python ⓘ |
| providesInterfaceSimilarTo |
NumPy
NERFINISHED
ⓘ
pandas ⓘ scikit-learn NERFINISHED ⓘ |
| repository | https://github.com/dask/dask ⓘ |
| supports |
arrays
ⓘ
dataframes ⓘ distributed computing ⓘ machine learning workflows ⓘ out-of-core computation ⓘ parallel computing ⓘ scalable analytics ⓘ task scheduling ⓘ |
| supportsClusterManager |
Kubernetes
NERFINISHED
ⓘ
PBS NERFINISHED ⓘ SLURM NERFINISHED ⓘ SSH clusters ⓘ YARN NERFINISHED ⓘ |
| supportsDataFormat |
CSV
NERFINISHED
ⓘ
HDF5 NERFINISHED ⓘ JSON NERFINISHED ⓘ ORC ⓘ Parquet NERFINISHED ⓘ |
| supportsExecutionModel |
dynamic task graphs
ⓘ
lazy evaluation ⓘ |
| supportsLanguage | Python ⓘ |
| usedFor |
ETL workflows
ⓘ
data engineering ⓘ data science ⓘ machine learning pipelines ⓘ |
| website | https://www.dask.org ⓘ |
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: Dask Description of subject: Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.