Triple

T17676288
Position Surface form Disambiguated ID Type / Status
Subject BLAS E440649 entity
Predicate hasImplementation P3697 FINISHED
Object Apple Accelerate framework 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: Apple Accelerate framework | Statement: [BLAS, hasImplementation, Apple Accelerate framework]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Apple Accelerate framework
Context triple: [BLAS, hasImplementation, Apple Accelerate framework]
  • A. Core Video
    Core Video is a macOS multimedia framework that provides efficient, low-latency video processing and display services for applications.
  • B. 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.
  • C. Apple graphics and compute ecosystem
    Apple graphics and compute ecosystem is the integrated stack of hardware, drivers, and software frameworks (like Metal and related tools) that powers graphics rendering and GPU-accelerated computation across Apple devices.
  • D. Swift for TensorFlow
    Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
  • E. Quartz Extreme
    Quartz Extreme is a graphics acceleration technology in macOS that offloads window compositing and interface rendering to the computer’s GPU for smoother visual performance.
  • 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: Apple Accelerate framework
Target entity description: Apple Accelerate framework is a high-performance macOS and iOS library that provides optimized mathematical, signal processing, and image processing routines for efficient numerical computing on Apple hardware.
  • A. Core Video
    Core Video is a macOS multimedia framework that provides efficient, low-latency video processing and display services for applications.
  • B. 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.
  • C. Apple graphics and compute ecosystem
    Apple graphics and compute ecosystem is the integrated stack of hardware, drivers, and software frameworks (like Metal and related tools) that powers graphics rendering and GPU-accelerated computation across Apple devices.
  • D. Swift for TensorFlow
    Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
  • E. Quartz Extreme
    Quartz Extreme is a graphics acceleration technology in macOS that offloads window compositing and interface rendering to the computer’s GPU for smoother visual performance.
  • 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_69d8b9e940b081908b862bb0e6e89b0d completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e46f6d9ab88190ab0e25eac8b0101c completed April 19, 2026, 6 a.m.
Created at: April 10, 2026, 10 a.m.