Triple
T26966650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BPP |
E679189
|
entity |
| Predicate | errorReductionProperty |
P161383
|
FINISHED |
| Object | error can be reduced exponentially by repetition |
—
|
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: error can be reduced exponentially by repetition | Statement: [BPP, errorReductionProperty, error can be reduced exponentially by repetition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: errorReductionProperty Context triple: [BPP, errorReductionProperty, error can be reduced exponentially by repetition]
-
A.
errorReductionGoal
Indicates that an entity has a target or objective to decrease the number or rate of errors relative to a current or baseline level.
-
B.
noiseReductionFeature
Indicates that an entity includes or supports a capability to reduce or minimize unwanted noise.
-
C.
targetReduction
Indicates a relationship where one entity is intended or expected to decrease, diminish, or lessen another entity by a specified amount or proportion.
-
D.
noiseReductionType
Indicates the specific method or technique used to reduce or minimize noise in a given context.
-
E.
restrictionProperty
Indicates a constraining characteristic or condition that limits or governs how another property or relationship may be applied or interpreted.
- 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_69eeeb4f3a448190b1e94b2d4776c16e |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f621210b788190ab9e910cd635f366 |
completed | May 2, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69f611af72ac819094598dd2530d7411 |
completed | May 2, 2026, 3:01 p.m. |
| PDg | Predicate description generation | batch_69f6125e54e0819088ee33a20efcc9e6 |
completed | May 2, 2026, 3:03 p.m. |
Created at: April 27, 2026, 6:36 a.m.