Triple
T18704709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple developer tools |
E457340
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object | Create ML app |
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 app | Statement: [Apple developer tools, includes, Create ML app]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Create ML app Context triple: [Apple developer tools, includes, Create ML app]
-
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.
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.
-
E.
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.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5671665bc8190b9b4a4ce4ec5b2eb |
completed | April 19, 2026, 11:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a052b38ab548190b46ecda128e93c9b |
completed | May 14, 2026, 1:54 a.m. |
Created at: April 10, 2026, 11:49 a.m.