Triple
T17499741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AWS Auto Scaling |
E426158
|
entity |
| Predicate | supportsResourceType |
P24486
|
FINISHED |
| Object | Amazon Custom Resources via 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: Amazon Custom Resources via Application Auto Scaling | Statement: [AWS Auto Scaling, supportsResourceType, Amazon Custom Resources via Application Auto Scaling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon Custom Resources via Application Auto Scaling Context triple: [AWS Auto Scaling, supportsResourceType, Amazon Custom Resources via 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.
AWS CDK
AWS CDK (AWS Cloud Development Kit) is an open-source software development framework that lets you define cloud infrastructure in familiar programming languages and synthesize it into AWS CloudFormation templates.
-
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.
AWS CloudFormation
AWS CloudFormation is an infrastructure-as-code service that lets users model, provision, and manage AWS and third-party resources using declarative templates.
-
E.
AWS App Mesh
AWS App Mesh is a service mesh that provides application-level networking to standardize and manage communication, observability, and traffic control for microservices running on AWS.
- 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: Amazon Custom Resources via Application Auto Scaling Target entity description: Amazon Custom Resources via Application Auto Scaling is a capability that lets you define and manage your own scalable resources using the Application Auto Scaling service, enabling custom workloads to automatically adjust capacity based on demand.
-
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.
AWS CDK
AWS CDK (AWS Cloud Development Kit) is an open-source software development framework that lets you define cloud infrastructure in familiar programming languages and synthesize it into AWS CloudFormation templates.
-
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.
AWS CloudFormation
chosen
AWS CloudFormation is an infrastructure-as-code service that lets users model, provision, and manage AWS and third-party resources using declarative templates.
-
E.
AWS App Mesh
AWS App Mesh is a service mesh that provides application-level networking to standardize and manage communication, observability, and traffic control for microservices running on AWS.
- 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.