Triple

T8415085
Position Surface form Disambiguated ID Type / Status
Subject Core ML E198712 entity
Predicate integratesWith P1075 FINISHED
Object Create ML E732968 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: Create ML | Statement: [Core ML, integratesWith, Create ML]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Create ML
Context triple: [Core ML, integratesWith, Create ML]
  • A. Create ML chosen
    Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
  • B. 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.
  • C. Oracle Machine Learning
    Oracle Machine Learning is a suite of in-database machine learning algorithms and tools from Oracle that enables data scientists and analysts to build, deploy, and manage predictive models directly within Oracle databases.
  • D. 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.
  • E. AutoML
    AutoML is a set of machine learning tools and services that automatically build, train, and optimize models with minimal manual coding or expertise.
  • 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_69ca831201b481909e137936ef99ff11 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb83e443a08190983d9a0a61e0f781 completed March 31, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce1d3d9f848190bd425b80aa58a376 completed April 2, 2026, 7:39 a.m.
Created at: March 30, 2026, 6:06 p.m.