Triple

T7858633
Position Surface form Disambiguated ID Type / Status
Subject Microsoft .NET documentation E182437 entity
Predicate topic P261 FINISHED
Object ML.NET E182435 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: ML.NET | Statement: [Microsoft .NET documentation, topic, ML.NET]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ML.NET
Context triple: [Microsoft .NET documentation, topic, ML.NET]
  • A. ML.NET chosen
    ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
  • B. Azure Machine Learning
    Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
  • C. Microsoft Cognitive Toolkit
    Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
  • D. Core ML
    Core ML is Apple’s machine learning framework that enables developers to integrate trained models efficiently into iOS, macOS, watchOS, and tvOS apps for on-device intelligence.
  • E. TensorFlow.js
    TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
  • 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_69ca82887fd48190975896bf38c4596b completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb1a787c8c8190bcd9ed76cc7aa4c5 completed March 31, 2026, 12:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b38d7e08190a3459d1e0c6f4c6d completed March 31, 2026, 5:27 a.m.
Created at: March 30, 2026, 4:52 p.m.