Triple
T400488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CPython |
E9268
|
entity |
| Predicate | runsOn |
P23
|
FINISHED |
| Object | macOS |
E6427
|
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: macOS | Statement: [CPython, runsOn, macOS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: macOS Context triple: [CPython, runsOn, macOS]
-
A.
macOS
chosen
macOS is Apple’s proprietary Unix-based operating system known for its graphical user interface, tight integration with Apple hardware and services, and strong emphasis on usability and security.
-
B.
Mac
Mac is Apple’s line of personal computers known for their sleek hardware design and tight integration with the macOS operating system.
-
C.
iPadOS
iPadOS is Apple’s tablet-focused operating system that builds on iOS with features and interfaces optimized for the iPad’s larger display and multitasking capabilities.
-
D.
iOS
iOS is Apple’s mobile operating system that powers iPhones and iPads, known for its integrated ecosystem, security features, and curated App Store.
-
E.
Apple silicon
Apple silicon is Apple’s custom family of ARM-based system-on-a-chip processors that power modern Macs and other Apple devices, offering high performance with improved energy efficiency.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ec8e655c819081eff85c0ef55fa5 |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4429ec70c8190aaff2e0e6af82612 |
completed | March 1, 2026, 1:43 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.