Triple
T11352025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple platforms APIs |
E268859
|
entity |
| Predicate | includesFramework |
P1393
|
FINISHED |
| Object | SceneKit |
E209895
|
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: SceneKit | Statement: [Apple platforms APIs, includesFramework, SceneKit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SceneKit Context triple: [Apple platforms APIs, includesFramework, SceneKit]
-
A.
SceneKit
chosen
SceneKit is a high-level 3D graphics framework from Apple used to build and render interactive 3D scenes and animations across its platforms.
-
B.
SpriteKit
SpriteKit is Apple’s 2D game development framework designed for building high-performance, animated games and interactive content across its platforms.
-
C.
RealityKit
RealityKit is Apple’s high-level 3D rendering and augmented reality framework used to build immersive spatial experiences on platforms like visionOS.
-
D.
ARKit framework
ARKit framework is Apple’s augmented reality development platform that enables iOS apps to blend virtual content with the real world using device cameras and motion sensors.
-
E.
SCNScene
SCNScene is a core SceneKit class that represents and manages a 3D scene graph, including its nodes, geometry, lights, and cameras.
- 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea24489081908fbf47fd2e6d709c |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e543a6e97481909dc77a553b217b4d |
completed | April 19, 2026, 9:05 p.m. |
Created at: April 8, 2026, 9:33 p.m.