Triple
T16076533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LINPACK |
E389991
|
entity |
| Predicate | precisionSupport |
P60855
|
FINISHED |
| Object | single precision |
—
|
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: single precision | Statement: [LINPACK, precisionSupport, single precision]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: precisionSupport Context triple: [LINPACK, precisionSupport, single precision]
-
A.
supportsPrecisionLevels
chosen
Indicates that one entity is capable of operating at, or accommodating, multiple specified levels of precision in relation to another entity or process.
-
B.
supportsArbitraryPrecisionArithmetic
Indicates that the subject system or component can perform arithmetic operations with numbers of virtually unlimited size and precision, beyond fixed hardware-imposed limits.
-
C.
supportsFloatingPoint
Indicates that an entity is capable of handling or operating with floating-point (non-integer) numeric values.
-
D.
supportsExactRationalArithmetic
Indicates that an entity provides operations on rational numbers using exact arithmetic without rounding or approximation.
-
E.
precision
Indicates the degree to which an action, measurement, or outcome is carried out with exactness, minimal deviation, and fine-grained accuracy.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1827ad7c88190b867da511cbfb7fa |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:57 a.m.