Triple

T19452057
Position Surface form Disambiguated ID Type / Status
Subject OS X El Capitan E486634 entity
Predicate kernelName P744 FINISHED
Object XNU 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: XNU | Statement: [OS X El Capitan, kernelName, XNU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: XNU
Context triple: [OS X El Capitan, kernelName, XNU]
  • A. XNU chosen
    XNU is the hybrid operating system kernel developed by Apple that powers macOS and other Apple platforms, combining components from Mach and BSD.
  • B. I/O Kit
    I/O Kit is macOS and iOS’s object-oriented driver framework that manages hardware devices and their interaction with the operating system.
  • C. Mach microkernel
    Mach microkernel is a pioneering microkernel-based operating system kernel developed at Carnegie Mellon University, known for its message-passing architecture and influence on systems like NeXTSTEP and early versions of macOS.
  • D. NeXTSTEP
    NeXTSTEP was an advanced Unix-based operating system and development environment created by NeXT Inc., notable for its object-oriented frameworks and influential role in the later development of macOS and iOS.
  • E. QNX
    QNX is a commercial, real-time, microkernel-based operating system widely used in embedded and safety-critical applications such as automotive, industrial, and medical systems.
  • 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_69d8e8d86d608190bd199a98d0297f27 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63392aab08190aab09b7c356e5b10 completed April 20, 2026, 2:09 p.m.
Created at: April 10, 2026, 1:38 p.m.