Triple
T7664722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMIX |
E173597
|
entity |
| Predicate | hasRegisterCount |
P12007
|
FINISHED |
| Object | 256 general-purpose 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: 256 general-purpose registers | Statement: [MMIX, hasRegisterCount, 256 general-purpose registers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegisterCount Context triple: [MMIX, hasRegisterCount, 256 general-purpose registers]
-
A.
hasRegister
Indicates that one entity possesses, contains, or is associated with a specific register (such as a record, log, or hardware register).
-
B.
registerCount
chosen
Indicates the number of registers associated with or allocated to a given entity in a system.
-
C.
segmentRegisterCount
Indicates the number of register units associated with or allocated to a particular segment in a system or structure.
-
D.
hasRegisterSystem
Indicates that an entity uses or is associated with a particular register system (e.g., a system for recording, tracking, or registering items, events, or participants).
-
E.
hasStandardRegister
Indicates that something is expressed or occurs in a standard, neutral, or non-marked linguistic register.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7063dab1881909598b04999b8b690 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015f7430819099d3ea2781b7cee2 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4 p.m.