Correct Counter
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Correct Counter has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
appliedToApplied to(1)
- Counter Increment
ex:counter-increment
executedOnExecuted on(1)
- Increment Action
ex:increment-action
initializesInitializes(1)
- Evaluate Accuracy
ex:evaluate-accuracy
modifiesVariableModifies Variable(1)
- Increment Operator
ex:increment-operator
normalizesCountNormalizes Count(1)
- Percentage Calculation
ex:percentage-calculation
numeratorNumerator(1)
- Accuracy Calculation
ex:accuracy-calculation
targetsTargets(1)
- Correct Increment
ex:correct-increment
Other facts (4)
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 | Integer Variable | [1] |
| Rdf:type | Variable | [2] |
| Is Incremented | On Condition Match | [1] |
| Initial Value | 0 | [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/9fb13580-dd5d-40ca-997b-58429581d55c- full textbeam-chunktext/plain1 KB
doc:beam/9fb13580-dd5d-40ca-997b-58429581d55cShow excerpt
for meta, gt in zip(metadata, ground_truth): if all(meta[key] == gt[key] for key in gt.keys()): correct += 1 return (correct / total) * 100 # Example ground truth data ground_truth = [...] # list of dictionarie…
ctx:claims/beam/e8aa5db9-3e5f-4e4b-b042-f2179d9b2b8f- full textbeam-chunktext/plain1 KB
doc:beam/e8aa5db9-3e5f-4e4b-b042-f2179d9b2b8fShow excerpt
tokenized_texts = [tokenize_text(text) for text in texts] # Evaluate accuracy def evaluate_accuracy(tokenized_texts, ground_truth): correct = 0 total = 0 for tokenized, truth in zip(tokenized_texts, ground_truth): for t…
See also
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