Triple
T17499733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AWS Auto Scaling |
E426158
|
entity |
| Predicate | supportsResourceType |
P24486
|
FINISHED |
| Object | Amazon SageMaker |
—
|
NE NERFINISHED |
How this triple was built (2 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 SageMaker | Statement: [AWS Auto Scaling, supportsResourceType, Amazon SageMaker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon SageMaker Context triple: [AWS Auto Scaling, supportsResourceType, Amazon SageMaker]
-
A.
Amazon SageMaker
chosen
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
-
B.
SageMaker Serverless Inference
SageMaker Serverless Inference is an AWS machine learning deployment option that automatically provisions and scales compute resources to host models for inference without requiring users to manage servers or infrastructure.
-
C.
SageMaker Studio
SageMaker Studio is Amazon SageMaker’s web-based integrated development environment (IDE) for building, training, and deploying machine learning models at scale.
-
D.
SageMaker Real-time Inference
SageMaker Real-time Inference is a managed Amazon SageMaker capability that lets you deploy machine learning models as always-on, low-latency APIs for real-time prediction workloads.
-
E.
SageMaker Profiler
SageMaker Profiler is a performance profiling tool in Amazon SageMaker that helps analyze and optimize the resource usage and efficiency of machine learning training jobs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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.