Triple
T20221265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple WWDC 2018 |
E495259
|
entity |
| Predicate | announced |
P429
|
FINISHED |
| Object | Core ML 2 |
—
|
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: Core ML 2 | Statement: [Apple WWDC 2018, announced, Core ML 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Core ML 2 Context triple: [Apple WWDC 2018, announced, Core ML 2]
-
A.
Core ML
chosen
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.
-
B.
TensorFlow Metal
TensorFlow Metal is an integration that enables TensorFlow to run efficiently on Apple GPUs via the Metal framework, accelerating machine learning workloads on macOS and iOS devices.
-
C.
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.
-
D.
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.
-
E.
Apple AI/ML
Apple AI/ML is Apple’s artificial intelligence and machine learning division, responsible for developing and integrating AI technologies across the company’s products and services.
- 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.