Triple

T20221266
Position Surface form Disambiguated ID Type / Status
Subject Apple WWDC 2018 E495259 entity
Predicate announced P429 FINISHED
Object Create ML 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: Create ML | Statement: [Apple WWDC 2018, announced, Create ML]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Create ML
Context triple: [Apple WWDC 2018, announced, 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. Neptune ML
    Neptune ML is a machine learning capability for Amazon Neptune that enables users to build and run graph neural network models directly on graph data stored in the database.
  • D. 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.
  • E. 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.
  • 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_69da626cff80819097b530718a7c98b6 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66fd610f881908fdd22b1f8bd2efc completed April 20, 2026, 6:26 p.m.
Created at: April 11, 2026, 11:39 p.m.