Triple
T19771984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cascade-Correlation learning architecture |
E474908
|
entity |
| Predicate | usesOptimizationCriterion |
P27210
|
FINISHED |
| Object | maximization of correlation between unit output and network residual error |
—
|
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: maximization of correlation between unit output and network residual error | Statement: [Cascade-Correlation learning architecture, usesOptimizationCriterion, maximization of correlation between unit output and network residual error]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesOptimizationCriterion Context triple: [Cascade-Correlation learning architecture, usesOptimizationCriterion, maximization of correlation between unit output and network residual error]
-
A.
supportsOptimizationAlgorithm
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
B.
canBeOptimizedFor
Indicates that one entity is capable of being improved or adjusted to perform better with respect to another specified criterion, context, or target.
-
C.
typeOfOptimality
chosen
Indicates that one entity specifies the particular notion or criterion of optimality that characterizes another entity’s optimal status or solution.
-
D.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
E.
optimizationTarget
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6535ce4d08190a1dfca2df95a8631 |
completed | April 20, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e53053ed2881908400becdfada7fd3 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.