True Vector
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
True Vector has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:is calculated from(2), is output of(1), computed from(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedIs Calculated Fromin disputeisCalculatedFrom
- Ground Truth Documents[2]sourceall time · Ca2653b8 C25f 4a54 Bdfa Ff6ea71f5472
- Retrieved Documents[2]sourceall time · Ca2653b8 C25f 4a54 Bdfa Ff6ea71f5472
Is Output ofisOutputOf
- True Vector Calculation[2]sourceall time · Ca2653b8 C25f 4a54 Bdfa Ff6ea71f5472
Computed FromcomputedFrom
- Ground Truth Documents[1]sourceall time · F008f4ce 021d 4be6 B191 62e598ae1493
Rdf:typerdf:type
Inbound mentions (4)
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.
accumulatesAccumulates(1)
- Y True
ex:y_true
extendedByExtended by(1)
- Y True
ex:y_true
hasVariableHas Variable(1)
- Code Snippet
ex:code-snippet
isExtendedByIs Extended by(1)
- Y True
ex:y_true
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)
- custom
ctx:claims/beam/f008f4ce-021d-4be6-b191-62e598ae1493- full textbeam-chunktext/plain1 KB
doc:beam/f008f4ce-021d-4be6-b191-62e598ae1493Show excerpt
dataset = pd.read_csv('queries_dataset.csv') # Split the dataset into training and testing sets train_data, test_data = train_test_split(dataset, test_size=0.2) # Train the RAG system (if needed) # ... # Evaluate the system on the test d…
- custom
ctx:claims/beam/ca2653b8-c25f-4a54-bdfa-ff6ea71f5472- full textbeam-chunktext/plain1 KB
doc:beam/ca2653b8-c25f-4a54-bdfa-ff6ea71f5472Show 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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