Triple

T3167484
Position Surface form Disambiguated ID Type / Status
Subject AIM E66246 entity
Predicate platform P1292 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: [AIM, platform, macOS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: macOS
Context triple: [AIM, platform, 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. Mac
    Mac is one of the main characters on the sitcom "It's Always Sunny in Philadelphia," known for his delusional self-image, obsession with toughness and martial arts, and often misguided religious zeal.
  • D. macOS Catalina
    macOS Catalina is a version of Apple's Mac operating system that introduced features like Sidecar for using an iPad as a second display, enhanced security, and the replacement of iTunes with separate media apps.
  • E. macOS Sierra
    macOS Sierra is a version of Apple's Mac operating system that focused on tighter integration with iOS, including features like Siri support and cross-device continuity tools.
  • 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_69ad8585d7988190af37365331093ccd completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada6457acc8190b2b9acbd1cfcdb91 completed March 8, 2026, 4:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24b552b488190938ed9fe4d046b01 completed March 12, 2026, 5:12 a.m.
Created at: March 8, 2026, 3:06 p.m.