Y True
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Y True has 8 facts recorded in Dontopedia across 2 references, with 2 live disagreements.
Mostly:rdf:type(2), contains(2), data type(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (11)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
takesArgumentTakes Argument(2)
- Precision Score
ex:precision_score - Recall Score
ex:recall_score
takesArgumentsTakes Arguments(2)
- Precision Score
ex:precision-score - Recall Score
ex:recall-score
assignsToListAssigns to List(1)
- Evaluation Execution
ex:evaluation-execution
calculatedFromCalculated From(1)
- Evaluation Metrics
ex:evaluation-metrics
definesDefines(1)
- Array Definition
ex:array-definition
extendsExtends(1)
- Evaluation Code
ex:evaluation-code
hasParameterHas Parameter(1)
- Evaluate Function
ex:evaluate-function
initializesInitializes(1)
- Evaluation Execution
ex:evaluation-execution
usesUses(1)
- Metric Calculation
ex:metric-calculation
Other facts (8)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Numpy Array | [1] |
| Rdf:type | Data Structure | [2] |
| Contains | 1 | [1] |
| Contains | 0 | [1] |
| Data Type | numpy.ndarray | [1] |
| Semantic Role | true-labels | [1] |
| Accumulates | True Vector | [2] |
| Is Accumulator for | True Vector | [2] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (2)
ctx:claims/beam/cbee7f04-fd50-4aaa-94fb-0a508b493da6ctx:claims/beam/4b0e94ef-084d-4363-8931-568f755392e6- full textbeam-chunktext/plain1 KB
doc:beam/4b0e94ef-084d-4363-8931-568f755392e6Show excerpt
true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …
See also
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