Triple
T28610476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TCG (Tiny Code Generator) |
E724156
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | dynamic binary translator |
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 translator Context triple: [TCG (Tiny Code Generator), instanceOf, dynamic binary translator]
-
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.
disassembler
A disassembler is a tool that translates low-level machine code or bytecode back into a human-readable assembly language representation for analysis or debugging.
-
C.
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.
-
D.
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.
-
E.
register-based virtual machine
A register-based virtual machine is an abstract computing model that executes instructions by operating primarily on a fixed set of virtual registers rather than a stack.
- 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.