Triple
T35077982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portable Native Client |
E1012355
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | portable subset of LLVM bitcode for NaCl |
C31226
|
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: portable subset of LLVM bitcode for NaCl Context triple: [Portable Native Client, instanceOf, portable subset of LLVM bitcode for NaCl]
-
A.
LLVM feature
chosen
An LLVM feature is a specific capability or extension within the LLVM compiler infrastructure that provides additional functionality for code analysis, optimization, transformation, or target-specific code generation.
-
B.
LLVM component
An LLVM component is a modular part of the LLVM compiler infrastructure that provides specific functionality—such as code analysis, optimization, or target code generation—within the overall compilation pipeline.
-
C.
LLVM sanitizer
An LLVM sanitizer is a runtime instrumentation tool integrated into the LLVM compiler framework that detects specific classes of bugs (such as memory errors, data races, or undefined behavior) by inserting diagnostic checks into compiled programs.
-
D.
binary translation technology
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.
-
E.
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.
- 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_69f76dd32c008190853aef6028f60208 |
completed | May 3, 2026, 3:46 p.m. |
Created at: May 3, 2026, 4:01 p.m.