Triple
T8284652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CP1 floating-point coprocessor |
E193759
|
entity |
| Predicate | coprocessorNumber |
P82502
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [CP1 floating-point coprocessor, coprocessorNumber, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coprocessorNumber Context triple: [CP1 floating-point coprocessor, coprocessorNumber, 1]
-
A.
numericCoprocessorSupport
Indicates that a system or processor includes and can utilize a dedicated numeric coprocessor for performing arithmetic or mathematical operations.
-
B.
numberOfPartitions
Indicates the total count of distinct partitions into which a given set, resource, or structure is divided.
-
C.
numberOfGeneralPurposeRegisters
Indicates the quantity of general-purpose registers associated with or available in a given computing context.
-
D.
coreCountCPU
Indicates the number of processing cores that a CPU has.
-
E.
numberOfCoreEngines
Indicates the quantity of primary engines associated with an entity.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69cb76d648988190ab0669cc0592e827 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 5:52 p.m.