[1, 0, 0]
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
[1, 0, 0] has 12 facts recorded in Dontopedia across 3 references, with 4 live disagreements.
Mostly:has component(3), rdf:type(2), has value at index(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (1)
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.
ex:queryVectorIdentityEx:query Vector Identity(1)
- Turn 8920
ex:turn-8920
Other facts (10)
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 |
|---|---|---|
| Has Component | First Vector Component 1 | [3] |
| Has Component | First Vector Component 2 | [3] |
| Has Component | First Vector Component 3 | [3] |
| Rdf:type | Numpy Array | [2] |
| Rdf:type | Vector | [3] |
| Has Value at Index | 1 | [2] |
| Has Value at Index | 0 | [2] |
| Coordinates | [0.1,0.2,0.3] | [1] |
| Characteristic | Sparse | [2] |
| Has Dimension | 3 | [3] |
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 (3)
ctx:claims/beam/92f9d4b6-659a-439c-ae2a-0330d3d8ab30- full textbeam-chunktext/plain1 KB
doc:beam/92f9d4b6-659a-439c-ae2a-0330d3d8ab30Show excerpt
'vector': [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]] } # Create a DataFrame to store the data df = pd.DataFrame(data) # Connect to MongoDB client = MongoClient('mongodb://localhost:27017/') db = client['rag_db'] collection = …
ctx:claims/beam/078a77df-55b0-4824-8111-4d77ab0c96e1- full textbeam-chunktext/plain1 KB
doc:beam/078a77df-55b0-4824-8111-4d77ab0c96e1Show excerpt
new_vectors[:self.capacity] = self.vectors self.vectors = new_vectors self.capacity = new_capacity # Example usage: vector_size = 3 vectorizer = SparseVectorizer(vector_size) vectorizer.add_vector(np.array([1, 0, 0]…
ctx:claims/beam/845a6907-ed34-463a-9173-bf20dfde1501- full textbeam-chunktext/plain1 KB
doc:beam/845a6907-ed34-463a-9173-bf20dfde1501Show excerpt
FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True), FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Test Collection") # Create a collection collectio…
See also
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