Triple

T12322262
Position Surface form Disambiguated ID Type / Status
Subject Amazon SageMaker E293756 entity
Predicate hasFeature P182 FINISHED
Object SageMaker Hyperparameter Tuning Jobs E293756 NE FINISHED

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: SageMaker Hyperparameter Tuning Jobs | Statement: [Amazon SageMaker, hasFeature, SageMaker Hyperparameter Tuning Jobs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SageMaker Hyperparameter Tuning Jobs
Context triple: [Amazon SageMaker, hasFeature, SageMaker Hyperparameter Tuning Jobs]
  • 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. Kubeflow Pipelines
    Kubeflow Pipelines is a platform for building, deploying, and managing end-to-end machine learning workflows on Kubernetes using containerized components.
  • C. Turi Create
    Turi Create is an open-source Python library from Apple that simplifies building, training, and deploying machine learning models, especially for use with Apple’s Core ML framework.
  • D. AutoML
    AutoML is a set of machine learning tools and services that automatically build, train, and optimize models with minimal manual coding or expertise.
  • E. Hugging Face Accelerate
    Hugging Face Accelerate is a lightweight library that simplifies running and scaling PyTorch and Transformers models across CPUs, GPUs, and distributed hardware with minimal code changes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4d7dac81909ff10e64e229ef33 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e8aa94881908e4c184062037ab5 completed May 2, 2026, 3:55 p.m.
Created at: April 8, 2026, 9:53 p.m.