Triple
T12280923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | compiler-rt |
E292713
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | ThreadSanitizer |
E292716
|
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: ThreadSanitizer | Statement: [compiler-rt, supports, ThreadSanitizer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ThreadSanitizer Context triple: [compiler-rt, supports, ThreadSanitizer]
-
A.
ThreadSanitizer
chosen
ThreadSanitizer is a dynamic analysis tool that detects data races and threading errors in concurrent programs, commonly used within the LLVM/Clang toolchain.
-
B.
MemorySanitizer
MemorySanitizer is an LLVM-based dynamic analysis tool that detects uses of uninitialized memory in programs at runtime.
-
C.
AddressSanitizer
AddressSanitizer is a fast memory error detector that instruments programs to find issues like buffer overflows and use-after-free bugs at runtime.
-
D.
Thread
Thread is a low-power, IPv6-based wireless mesh networking protocol designed primarily for secure and reliable communication among smart home and IoT devices.
-
E.
Threads
Threads is a social media app by Meta designed for real-time text-based conversations and sharing among users, closely integrated with Instagram.
- 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_69f61e70dec8819098199fbb54d888c1 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.