Triple

T17499734
Position Surface form Disambiguated ID Type / Status
Subject AWS Auto Scaling E426158 entity
Predicate supportsResourceType P24486 FINISHED
Object Amazon Comprehend — 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 Comprehend | Statement: [AWS Auto Scaling, supportsResourceType, Amazon Comprehend]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Amazon Comprehend
Context triple: [AWS Auto Scaling, supportsResourceType, Amazon Comprehend]
  • A. Google Natural Language API
    Google Natural Language API is a cloud-based service that uses machine learning to analyze and understand text, offering features like sentiment analysis, entity recognition, syntax parsing, and content classification.
  • B. NLU
    NLU is the IATA airport code for Felipe Ángeles International Airport, a major commercial airport serving the Mexico City metropolitan area.
  • C. IBM Watson Discovery
    IBM Watson Discovery is an AI-powered enterprise search and text analytics platform that uses natural language processing to extract insights from large volumes of unstructured data.
  • D. Stanford CoreNLP
    Stanford CoreNLP is a widely used, open-source natural language processing toolkit that provides a broad range of linguistic analysis tools such as tokenization, parsing, and named entity recognition.
  • E. Azure Cognitive Services
    Azure Cognitive Services is a suite of cloud-based AI APIs and tools that enable developers to add capabilities like vision, speech, language understanding, and decision-making to their applications without needing deep machine learning expertise.
  • 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 Comprehend
Target entity description: Amazon Comprehend is a fully managed natural language processing (NLP) service from AWS that uses machine learning to extract insights such as sentiment, key phrases, entities, and topics from text.
  • A. Google Natural Language API
    Google Natural Language API is a cloud-based service that uses machine learning to analyze and understand text, offering features like sentiment analysis, entity recognition, syntax parsing, and content classification.
  • B. NLU
    NLU is the IATA airport code for Felipe Ángeles International Airport, a major commercial airport serving the Mexico City metropolitan area.
  • C. IBM Watson Discovery
    IBM Watson Discovery is an AI-powered enterprise search and text analytics platform that uses natural language processing to extract insights from large volumes of unstructured data.
  • D. Stanford CoreNLP
    Stanford CoreNLP is a widely used, open-source natural language processing toolkit that provides a broad range of linguistic analysis tools such as tokenization, parsing, and named entity recognition.
  • E. Azure Cognitive Services
    Azure Cognitive Services is a suite of cloud-based AI APIs and tools that enable developers to add capabilities like vision, speech, language understanding, and decision-making to their applications without needing deep machine learning expertise.
  • F. None of above. chosen

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.