Triple
T17676578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mersenne Twister |
E440656
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | MT19937-64 |
—
|
NE NERFINISHED |
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: MT19937-64 | Statement: [Mersenne Twister, hasVariant, MT19937-64]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MT19937-64 Context triple: [Mersenne Twister, hasVariant, MT19937-64]
-
A.
MersenneTwister
chosen
MersenneTwister is a widely used pseudorandom number generator algorithm known for its long period and high-quality statistical properties.
-
B.
MRG32k3a generator
The MRG32k3a generator is a high-quality combined multiple recursive pseudorandom number generator widely used in scientific computing and simulations for its long period and good statistical properties.
-
C.
TinyMT
TinyMT is a lightweight variant of the Mersenne Twister pseudorandom number generator designed for reduced memory usage and faster initialization while maintaining good statistical properties.
-
D.
Marsaglia
Marsaglia is a locality in northwestern Italy historically noted as the site of the 1693 Battle of Marsaglia during the Nine Years' War.
-
E.
cuRAND
cuRAND is NVIDIA's GPU-accelerated random number generation library designed to efficiently produce high-quality random numbers for parallel applications using CUDA.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6d9ab88190ab0e25eac8b0101c |
completed | April 19, 2026, 6 a.m. |
Created at: April 10, 2026, 10:01 a.m.