Triple

T8285705
Position Surface form Disambiguated ID Type / Status
Subject Qumranet E193781 entity
Predicate product P490 FINISHED
Object KVM hypervisor E37340 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: KVM hypervisor | Statement: [Qumranet, product, KVM hypervisor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: KVM hypervisor
Context triple: [Qumranet, product, KVM hypervisor]
  • A. KVM chosen
    KVM (Kernel-based Virtual Machine) is a Linux kernel module that turns the kernel into a hypervisor, enabling hardware-assisted virtualization of multiple virtual machines.
  • B. QEMU
    QEMU is an open-source machine emulator and virtualizer that enables running operating systems and programs for one hardware platform on another.
  • C. Hypervisor framework
    Hypervisor framework is Apple’s low-level virtualization API on macOS that lets developers create and manage virtual machines and run guest operating systems efficiently on Apple hardware.
  • D. Libvirt
    Libvirt is an open-source API, daemon, and management tool for managing platform virtualization technologies such as KVM, QEMU, and Xen.
  • E. VMX
    VMX is a vector processing extension to the PowerPC architecture designed to accelerate multimedia, signal processing, and other parallelizable computations.
  • 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_69ca82e32db481908b72f3804fa71152 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7ad20ae481908179aba245c73fad completed March 31, 2026, 7:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd688441908190b6b0a39dfb9d87ac completed April 1, 2026, 6:48 p.m.
Created at: March 30, 2026, 5:52 p.m.