Triple
T28610429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TCG |
E724155
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | dynamic binary translation engine |
C8850
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: dynamic binary translation engine Context triple: [TCG, instanceOf, dynamic binary translation engine]
-
A.
binary translation technology
chosen
Binary translation technology is a system that dynamically or statically converts compiled machine code from one instruction set architecture to another so that software can run unmodified on different hardware platforms.
-
B.
lightweight virtual machine technology
Lightweight virtual machine technology is a virtualization approach that provides isolated, minimal-footprint environments—often using shared kernels or stripped-down images—to run applications efficiently with reduced overhead compared to traditional virtual machines.
-
C.
runtime engine
A runtime engine is the software component that executes compiled or interpreted code, managing program instructions, memory, and system resources during execution.
-
D.
virtual machine bytecode
Virtual machine bytecode is a low-level, platform-independent instruction set executed by a virtual machine, serving as an intermediate representation between high-level source code and machine code.
-
E.
virtual machine monitor
A virtual machine monitor is a software or firmware layer that creates, manages, and isolates virtual machines by mediating access to the underlying hardware resources.
- F. None of above.
Provenance (1 batch)
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_69f01d816d7c8190a1fe27e3434041dc |
completed | April 28, 2026, 2:37 a.m. |
Created at: April 28, 2026, 4:29 a.m.