AWS X-Ray
E426157
AWS X-Ray is an AWS service that helps developers analyze and debug distributed applications by tracing requests as they travel through various microservices and resources.
All labels observed (1)
| Label | Occurrences |
|---|---|
| AWS X-Ray canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T4280187 — 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: AWS X-Ray Context triple: [Amazon CloudWatch, integratesWith, AWS X-Ray]
-
A.
Amazon CloudWatch
Amazon CloudWatch is a monitoring and observability service that collects and analyzes logs, metrics, and events from AWS resources and applications to help track performance and operational health.
-
B.
AppDynamics
AppDynamics is an application performance monitoring and observability company that provides tools to track, analyze, and optimize the performance of software applications and IT infrastructure.
-
C.
New Relic
New Relic is a software analytics and application performance monitoring company that provides tools for tracking and optimizing the performance of web and mobile applications.
-
D.
AWS CloudTrail
AWS CloudTrail is a logging and auditing service that records and monitors API calls and account activity across AWS resources for security, compliance, and operational troubleshooting.
-
E.
Amazon Kinesis
Amazon Kinesis is a fully managed AWS service for real-time collection, processing, and analysis of streaming data at scale.
- 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: AWS X-Ray Target entity description: AWS X-Ray is an AWS service that helps developers analyze and debug distributed applications by tracing requests as they travel through various microservices and resources.
-
A.
Amazon CloudWatch
Amazon CloudWatch is a monitoring and observability service that collects and analyzes logs, metrics, and events from AWS resources and applications to help track performance and operational health.
-
B.
AppDynamics
AppDynamics is an application performance monitoring and observability company that provides tools to track, analyze, and optimize the performance of software applications and IT infrastructure.
-
C.
New Relic
New Relic is a software analytics and application performance monitoring company that provides tools for tracking and optimizing the performance of web and mobile applications.
-
D.
AWS CloudTrail
AWS CloudTrail is a logging and auditing service that records and monitors API calls and account activity across AWS resources for security, compliance, and operational troubleshooting.
-
E.
Amazon Kinesis
Amazon Kinesis is a fully managed AWS service for real-time collection, processing, and analysis of streaming data at scale.
- F. None of above. chosen
Statements (68)
| Predicate | Object |
|---|---|
| instanceOf |
Amazon Web Services product
ⓘ
cloud computing service ⓘ distributed tracing service ⓘ |
| dataModel |
segments
ⓘ
service graph ⓘ subsegments ⓘ traces ⓘ |
| deploymentModel | cloud ⓘ |
| developer | Amazon Web Services ⓘ |
| feature |
SDKs for multiple programming languages
ⓘ
annotations ⓘ automatic instrumentation for some AWS services ⓘ encryption of trace data at rest ⓘ encryption of trace data in transit ⓘ end-to-end request tracing ⓘ filter expressions ⓘ integrations with AWS services ⓘ metadata ⓘ segment documents ⓘ service map ⓘ subsegments ⓘ trace analytics ⓘ trace sampling ⓘ |
| integratesWith |
AWS Elastic Beanstalk
ⓘ
AWS Lambda ⓘ Amazon API Gateway ⓘ Amazon DynamoDB ⓘ Amazon EC2 ⓘ Amazon ECS ⓘ Amazon EKS ⓘ
linked to:
Amazon Elastic Kubernetes Service
Amazon RDS ⓘ Amazon SNS ⓘ Amazon SQS ⓘ Elastic Load Balancing ⓘ
linked to:
AWS Elastic Load Balancing
|
| monitors |
errors
ⓘ
faults ⓘ latency ⓘ throttling ⓘ |
| offeredBy | Amazon Web Services ⓘ |
| pricingModel | pay-as-you-go ⓘ |
| primaryFunction |
application performance analysis
ⓘ
debugging distributed applications ⓘ distributed tracing ⓘ request tracing ⓘ |
| provider | Amazon Web Services ⓘ |
| regionAvailability | multiple AWS regions worldwide ⓘ |
| securityFeature |
AWS Identity and Access Management integration
ⓘ
AWS Key Management Service integration ⓘ |
| supportsArchitecture |
containerized applications
ⓘ
microservices ⓘ serverless applications ⓘ |
| supportsLanguage |
.NET
ⓘ
linked to:
.NET Framework
Go ⓘ Java ⓘ Node.js ⓘ Python ⓘ Ruby ⓘ |
| supportsPlatform | Amazon Web Services ⓘ |
| targetUser |
DevOps engineers
ⓘ
application developers ⓘ site reliability engineers ⓘ |
| useCase |
error analysis
ⓘ
latency analysis ⓘ microservices monitoring ⓘ root cause analysis ⓘ service map visualization ⓘ |
| visualizes |
request paths
ⓘ
service dependencies ⓘ |
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: AWS X-Ray Description of subject: AWS X-Ray is an AWS service that helps developers analyze and debug distributed applications by tracing requests as they travel through various microservices and resources.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.