Triple
T1775217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Neural Engine |
E38961
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object |
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.
|
E198712
|
NE FINISHED |
How this triple was built (4 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: Core ML | Statement: [Apple Neural Engine, supports, Core ML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Core ML Context triple: [Apple Neural Engine, supports, Core ML]
-
A.
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.
-
B.
ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
-
C.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
D.
Apple Neural Engine
Apple Neural Engine is Apple’s dedicated on-chip hardware accelerator designed to efficiently perform machine learning and AI computations on its devices.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Core ML Triple: [Apple Neural Engine, supports, Core ML]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Core ML Target entity description: 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.
-
A.
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.
-
B.
ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
-
C.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
D.
Apple Neural Engine
Apple Neural Engine is Apple’s dedicated on-chip hardware accelerator designed to efficiently perform machine learning and AI computations on its devices.
-
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. chosen
Provenance (5 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa64b6c4a88190ab2f75c8d4814f11 |
completed | March 6, 2026, 5:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada9982d208190b0c29ee1141e91b0 |
completed | March 8, 2026, 4:53 p.m. |
| NEDg | Description generation | batch_69adab03a5448190b42966adcd8afbde |
completed | March 8, 2026, 4:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adaeabff6c8190b19bc6478a28641c |
completed | March 8, 2026, 5:15 p.m. |
Created at: March 4, 2026, 7:31 p.m.