Triple
T25436415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | x87 FPU |
E637383
|
entity |
| Predicate | supportsRoundingMode |
P159805
|
FINISHED |
| Object | round to nearest |
—
|
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: round to nearest | Statement: [x87 FPU, supportsRoundingMode, round to nearest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsRoundingMode Context triple: [x87 FPU, supportsRoundingMode, round to nearest]
-
A.
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.
-
B.
roundingBehavior
chosen
Indicates how a value is adjusted to a nearby representable number according to a specific rounding rule (e.g., up, down, nearest).
-
C.
supportsExactRationalArithmetic
Indicates that an entity provides operations on rational numbers using exact arithmetic without rounding or approximation.
-
D.
supportsFloatingPoint
Indicates that an entity is capable of handling or operating with floating-point (non-integer) numeric values.
-
E.
supportsPrecisionLevels
Indicates that one entity is capable of operating at, or accommodating, multiple specified levels of precision in relation to another entity or process.
- 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_69e75db6c97081908178383fa632b193 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: April 21, 2026, 1:59 p.m.