Triple
T25425435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CLMUL |
E637104
|
entity |
| Predicate | operandSize |
P44870
|
FINISHED |
| Object | 64-bit lanes within 128-bit XMM registers |
—
|
LITERAL 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: 64-bit lanes within 128-bit XMM registers | Statement: [CLMUL, operandSize, 64-bit lanes within 128-bit XMM registers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operandSize Context triple: [CLMUL, operandSize, 64-bit lanes within 128-bit XMM registers]
-
A.
instructionSetSize
Indicates the size or number of instructions defined in an instruction set.
-
B.
inputSizeNotation
Indicates the notation or format used to express the size or dimensionality of an input.
-
C.
instructionLength
Indicates the duration or amount of time required to carry out a given instruction or operation.
-
D.
stateSizeBytes
Indicates the size of a given state or stateful data in terms of the number of bytes it occupies.
-
E.
hasWordSize
chosen
Indicates that an entity possesses or is characterized by a specific word length or word-based size.
- F. None of above.
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_69e75db58a1c8190891b9ff7c2f8414e |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f7221dc9a88190bb8194fcc29c42bc |
completed | May 3, 2026, 10:23 a.m. |
| PD | Predicate disambiguation | batch_69f72153a9188190b02adc84e1be4af8 |
completed | May 3, 2026, 10:20 a.m. |
Created at: April 21, 2026, 1:57 p.m.