Triple

T17499743
Position Surface form Disambiguated ID Type / Status
Subject AWS Auto Scaling E426158 entity
Predicate hasComponent P35 FINISHED
Object Application Auto Scaling NE NERFINISHED

How this triple was built (3 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Application Auto Scaling | Statement: [AWS Auto Scaling, hasComponent, Application Auto Scaling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Application Auto Scaling
Context triple: [AWS Auto Scaling, hasComponent, Application Auto Scaling]
  • A. AWS Auto Scaling
    AWS Auto Scaling is an Amazon Web Services feature that automatically adjusts the number of compute resources in response to changing application demand to maintain performance and optimize costs.
  • B. Application Load Balancer
    Application Load Balancer is an AWS Elastic Load Balancing service that intelligently routes HTTP/HTTPS traffic at the application layer, supporting advanced routing features for modern, microservices-based architectures.
  • C. AWS App Runner
    AWS App Runner is a fully managed service from Amazon Web Services that makes it easy to build, deploy, and run containerized web applications and APIs at scale without managing infrastructure.
  • D. Application Policy Infrastructure Controller
    The Application Policy Infrastructure Controller (APIC) is Cisco ACI’s centralized software controller that automates, manages, and monitors application-centric network policies across data center fabrics.
  • E. AWS Elastic Beanstalk
    AWS Elastic Beanstalk is a fully managed service for deploying and scaling web applications and services by automatically handling infrastructure provisioning, load balancing, scaling, and monitoring.
  • 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: Application Auto Scaling
Target entity description: Application Auto Scaling is an AWS service that automatically adjusts the capacity of scalable resources such as ECS services, DynamoDB tables, and custom applications based on defined policies and real-time demand.
  • A. AWS Auto Scaling chosen
    AWS Auto Scaling is an Amazon Web Services feature that automatically adjusts the number of compute resources in response to changing application demand to maintain performance and optimize costs.
  • B. Application Load Balancer
    Application Load Balancer is an AWS Elastic Load Balancing service that intelligently routes HTTP/HTTPS traffic at the application layer, supporting advanced routing features for modern, microservices-based architectures.
  • C. AWS App Runner
    AWS App Runner is a fully managed service from Amazon Web Services that makes it easy to build, deploy, and run containerized web applications and APIs at scale without managing infrastructure.
  • D. Application Policy Infrastructure Controller
    The Application Policy Infrastructure Controller (APIC) is Cisco ACI’s centralized software controller that automates, manages, and monitors application-centric network policies across data center fabrics.
  • E. AWS Elastic Beanstalk
    AWS Elastic Beanstalk is a fully managed service for deploying and scaling web applications and services by automatically handling infrastructure provisioning, load balancing, scaling, and monitoring.
  • F. None of above.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d889dd9164819087b1dc3c9240c870 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452112ff0819089c2951baba90102 completed April 19, 2026, 3:54 a.m.
Created at: April 10, 2026, 5:48 a.m.