Triple
T12281109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MemorySanitizer |
E292717
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | LLVM Sanitizers |
E292713
|
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: LLVM Sanitizers | Statement: [MemorySanitizer, partOf, LLVM Sanitizers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LLVM Sanitizers Context triple: [MemorySanitizer, partOf, LLVM Sanitizers]
-
A.
AddressSanitizer
AddressSanitizer is a fast memory error detector that instruments programs to find issues like buffer overflows and use-after-free bugs at runtime.
-
B.
LLVM
LLVM is a modular, reusable compiler and toolchain infrastructure project widely used for building language frontends, optimizers, and backends for diverse hardware architectures.
-
C.
compiler-rt
chosen
compiler-rt is the LLVM runtime library project that provides low-level compiler support routines such as builtins, sanitizers, and profiling helpers used by code generated with LLVM-based toolchains.
-
D.
MemorySanitizer
MemorySanitizer is an LLVM-based dynamic analysis tool that detects uses of uninitialized memory in programs at runtime.
-
E.
Clang-Tidy
Clang-Tidy is a C++ “linter” and static analysis tool that automatically detects and suggests fixes for common programming errors, style issues, and potential bugs in code.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cf2b09c81908a11581d33f65be0 |
completed | April 10, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a97614c8190b67e07df3e424e32 |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:52 p.m.