Triple
T21061111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GameplayKit |
E518847
|
entity |
| Predicate | namespace |
P23103
|
FINISHED |
| Object | GameplayKit |
—
|
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: GameplayKit | Statement: [GameplayKit, namespace, GameplayKit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GameplayKit Context triple: [GameplayKit, namespace, GameplayKit]
-
A.
GameplayKit
chosen
GameplayKit is an Apple game-development framework that provides tools for AI, pathfinding, state machines, and other core gameplay logic across iOS, macOS, and related 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.
GameKit
GameKit is Apple's framework that enables multiplayer gaming, leaderboards, achievements, and other social gaming features across its platforms.
-
D.
SceneKit
SceneKit is a high-level 3D graphics framework from Apple used to build and render interactive 3D scenes and animations across its platforms.
-
E.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
- 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_69e0b505ef108190b25dd4033e2ff7eb |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6feaf3edc81909423e039cac6bd87 |
completed | April 21, 2026, 4:35 a.m. |
Created at: April 16, 2026, 2:38 p.m.