Triple

T18800641
Position Surface form Disambiguated ID Type / Status
Subject Amazon EMR E459746 entity
Predicate integratesWith P1075 FINISHED
Object AWS Lake Formation 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: AWS Lake Formation | Statement: [Amazon EMR, integratesWith, AWS Lake Formation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AWS Lake Formation
Context triple: [Amazon EMR, integratesWith, AWS Lake Formation]
  • A. AWS Lake Formation chosen
    AWS Lake Formation is a managed AWS service that simplifies building, securing, and managing data lakes by centralizing data access control and governance across analytics services.
  • B. AWS Glue
    AWS Glue is a fully managed extract, transform, and load (ETL) service from Amazon Web Services that simplifies data preparation and integration for analytics and data warehousing.
  • C. Amazon Athena
    Amazon Athena is a serverless, interactive query service from AWS that lets users analyze data directly in Amazon S3 using standard SQL.
  • D. Azure Purview
    Azure Purview is a unified data governance and catalog service from Microsoft that helps organizations discover, classify, and manage data across on-premises, multi-cloud, and SaaS sources.
  • E. Amazon EMR
    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.
  • 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.