Triple
T1719613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyper-V |
E37359
|
entity |
| Predicate | managementTool |
P1652
|
FINISHED |
| Object | Hyper-V Manager |
E37359
|
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: Hyper-V Manager | Statement: [Hyper-V, managementTool, Hyper-V Manager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hyper-V Manager Context triple: [Hyper-V, managementTool, Hyper-V Manager]
-
A.
Hyper-V
chosen
Hyper-V is Microsoft's native hypervisor platform that enables the creation and management of virtual machines on Windows systems.
-
B.
VirtualBox
VirtualBox is a popular open-source virtualization platform that allows users to run multiple operating systems simultaneously on a single physical machine.
-
C.
VMware
VMware is a leading American cloud computing and virtualization technology company known for its pioneering hypervisor and software-defined data center solutions.
-
D.
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.
-
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_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa633934a4819083f2929da03453a8 |
completed | March 6, 2026, 5:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ae98cd88190af4dc46679b3d93f |
completed | March 8, 2026, 2:42 p.m. |
Created at: March 4, 2026, 7:30 p.m.