Triple

T18800660
Position Surface form Disambiguated ID Type / Status
Subject Amazon EMR E459746 entity
Predicate deploymentModel P9405 FINISHED
Object Amazon EMR on EKS 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 EMR on EKS | Statement: [Amazon EMR, deploymentModel, Amazon EMR on EKS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Amazon EMR on EKS
Context triple: [Amazon EMR, deploymentModel, Amazon EMR on EKS]
  • A. Amazon EMR chosen
    Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
  • B. Amazon MSK
    Amazon MSK is a fully managed Apache Kafka service from AWS that simplifies setting up, scaling, and operating Kafka clusters for streaming data applications.
  • C. Amazon Elastic Kubernetes Service
    Amazon Elastic Kubernetes Service (Amazon EKS) is a cloud-based, fully managed service from AWS for running and scaling containerized applications using Kubernetes.
  • D. Amazon ECS
    Amazon ECS is a fully managed container orchestration service that lets users run, scale, and secure Docker containers on AWS infrastructure.
  • E. Amazon Kinesis Data Analytics
    Amazon Kinesis Data Analytics is a fully managed AWS service that enables real-time processing and analysis of streaming data using SQL or Apache Flink.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a02332d88190b68feea7f2f86d06 completed April 20, 2026, 3:40 a.m.
Created at: April 10, 2026, 11:53 a.m.