GraphX
E705279
GraphX is Apache Spark’s distributed graph processing framework that enables large-scale graph computation and analysis using Spark’s resilient distributed datasets (RDDs).
All labels observed (1)
| Label | Occurrences |
|---|---|
| GraphX canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7984805 — 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: GraphX Context triple: [Apache Spark, component, GraphX]
-
A.
DGL
DGL is the vehicle registration code assigned to the town of Głogów in Poland.
-
B.
NetworkX
NetworkX is a Python library for creating, analyzing, and visualizing complex networks and graphs.
-
C.
Graph Algorithms (book)
"Graph Algorithms" is a foundational textbook by Shimon Even that systematically presents the theory, design, and analysis of algorithms for solving fundamental problems on graphs.
-
D.
BGL
BGL is the vehicle registration code for the Berchtesgadener Land district in the German state of Bavaria.
-
E.
DAG
DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
- 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: GraphX Target entity description: GraphX is Apache Spark’s distributed graph processing framework that enables large-scale graph computation and analysis using Spark’s resilient distributed datasets (RDDs).
-
A.
DGL
DGL is the vehicle registration code assigned to the town of Głogów in Poland.
-
B.
NetworkX
NetworkX is a Python library for creating, analyzing, and visualizing complex networks and graphs.
-
C.
Graph Algorithms (book)
"Graph Algorithms" is a foundational textbook by Shimon Even that systematically presents the theory, design, and analysis of algorithms for solving fundamental problems on graphs.
-
D.
BGL
BGL is the vehicle registration code for the Berchtesgadener Land district in the German state of Bavaria.
-
E.
DAG
DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
distributed computing framework
ⓘ
graph processing framework ⓘ |
| abbreviation | RDDs ⓘ |
| allows | viewing data as both graphs and collections ⓘ |
| basedOn | Resilient Distributed Datasets NERFINISHED ⓘ |
| category |
big data analytics
ⓘ
graph analytics ⓘ |
| designedFor | cluster computing environments ⓘ |
| developedBy | Apache Software Foundation NERFINISHED ⓘ |
| ecosystem | Apache Spark ecosystem ⓘ |
| executionEngine | Apache Spark NERFINISHED ⓘ |
| integratesWith |
Spark Core
NERFINISHED
ⓘ
Spark SQL NERFINISHED ⓘ Spark Streaming NERFINISHED ⓘ |
| languageSupport |
Java
NERFINISHED
ⓘ
Python NERFINISHED ⓘ Scala NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| partOf | Apache Spark NERFINISHED ⓘ |
| programmingModel | distributed graph processing ⓘ |
| provides |
graph algorithms library
ⓘ
graph operators ⓘ graph-parallel computation ⓘ optimized graph representation ⓘ property graph abstraction ⓘ |
| storageModel | RDD-based graph representation ⓘ |
| supports |
ETL for graphs
ⓘ
PageRank NERFINISHED ⓘ connected components ⓘ distributed data parallelism ⓘ edge-centric computation ⓘ fault-tolerant computation ⓘ graph joins ⓘ graph transformations ⓘ in-memory computation ⓘ interactive graph queries ⓘ iterative graph algorithms ⓘ label propagation ⓘ large-scale graph analysis ⓘ large-scale graph computation ⓘ shortest paths ⓘ triangle counting ⓘ vertex-centric computation ⓘ |
| useCase |
fraud detection
ⓘ
link analysis ⓘ network optimization ⓘ recommendation systems ⓘ social network analysis ⓘ |
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: GraphX Description of subject: GraphX is Apache Spark’s distributed graph processing framework that enables large-scale graph computation and analysis using Spark’s resilient distributed datasets (RDDs).
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.