Triple
T8415068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Core ML |
E198712
|
entity |
| Predicate | platform |
P1292
|
FINISHED |
| Object | watchOS |
E20923
|
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: watchOS | Statement: [Core ML, platform, watchOS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: watchOS Context triple: [Core ML, platform, watchOS]
-
A.
watchOS
chosen
watchOS is Apple’s smartwatch operating system that powers the Apple Watch, providing fitness tracking, notifications, and app functionality tightly integrated with iOS and the Apple ecosystem.
-
B.
WatchKit
WatchKit is Apple’s framework that enables developers to build and manage user interfaces and interactions for apps running on Apple Watch.
-
C.
Apple Watch
The Apple Watch is a smartwatch line that integrates closely with the iPhone to provide fitness tracking, health monitoring, notifications, and app functionality on the wrist.
-
D.
Wear OS
Wear OS is Google’s smartwatch operating system designed to bring Android apps, notifications, and Google services to wearable devices.
-
E.
visionOS
visionOS is Apple’s mixed-reality operating system designed for spatial computing on Apple Vision Pro, integrating 3D interfaces, gesture and eye tracking, and immersive app experiences.
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e443a08190983d9a0a61e0f781 |
completed | March 31, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce032a25ec819094c6346eb2a7f973 |
completed | April 2, 2026, 5:48 a.m. |
Created at: March 30, 2026, 6:06 p.m.