Triple
T8284606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | o32 ABI |
E193758
|
entity |
| Predicate | integerRegisterSize |
P44870
|
FINISHED |
| Object | 32 bits |
—
|
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: 32 bits | Statement: [o32 ABI, integerRegisterSize, 32 bits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: integerRegisterSize Context triple: [o32 ABI, integerRegisterSize, 32 bits]
-
A.
numberOfGeneralPurposeRegisters
Indicates the quantity of general-purpose registers associated with or available in a given computing context.
-
B.
instructionSetSize
Indicates the size or number of instructions defined in an instruction set.
-
C.
segmentRegisterCount
Indicates the number of register units associated with or allocated to a particular segment in a system or structure.
-
D.
numberOfIndexRegisters
Indicates the quantity of index registers associated with or available to a given entity (such as a processor or instruction set).
-
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_69ca82e217a48190880695635c44b2ed |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7ad0535081908bb234cfc0e32b32 |
completed | March 31, 2026, 7:42 a.m. |
| PD | Predicate disambiguation | batch_69cb70ad9fc081908741f8c4a4141edf |
completed | March 31, 2026, 6:58 a.m. |
Created at: March 30, 2026, 5:52 p.m.