Triple
T21494166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fermat primality test |
E530310
|
entity |
| Predicate | errorSide |
P144600
|
FINISHED |
| Object | primality side |
—
|
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: primality side | Statement: [Fermat primality test, errorSide, primality side]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: errorSide Context triple: [Fermat primality test, errorSide, primality side]
-
A.
errorType
Indicates the specific category or kind of error associated with an event, action, or entity.
-
B.
errorTerm
Indicates the specific discrepancy or residual value that quantifies the difference between an observed outcome and its predicted or true value in a model or calculation.
-
C.
errorPhase
Indicates the specific stage or phase in a process or workflow during which an error occurred.
-
D.
onSide
Indicates that one entity is positioned along or adjacent to the side of another entity.
-
E.
errorModel
Indicates the specific model or framework used to represent, quantify, or simulate errors in a process, system, or prediction.
- 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_69e0c45bd15481909fba5910765cdda2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea567244819091863350fedae3ae |
completed | April 23, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69e631f6e68081908f5ee4ce7413803e |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e6386c5a4481909c37f7de7e9fc025 |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 16, 2026, 6:23 p.m.