Dontopedia

List Extension

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

List Extension has 5 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

5 facts·3 predicates·3 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound 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.

containsPropertyContains Property(1)

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.

4 facts
PredicateValueRef
Rdf:typeList Operation[2]
Rdf:typeList Operation[3]
Has Value.py[1]
Performed inBatch Search Function[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.

hasValueblah/omega/841
.py
typebeam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
ex:ListOperation
labelbeam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
List Extension
performedInbeam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
ex:batch-search-function
typebeam/dad116a3-2105-43a3-93d8-198911a2b349
ex:ListOperation

References (3)

3 references
  1. [1]8411 fact
    ctx:discord/blah/omega/841
    • full textomega-841
      text/plain2 KBdoc:agent/omega-841/2d91c399-630a-4a30-a9ec-dc1d3bd18a01
      Show excerpt
      [2026-01-12 20:51] omega [bot]: You can upload your full mairy_pipeline.py (or other documents) as a file attachment here in Discord. I can then download and parse the file to analyze it in full, much more efficiently than pasting chunks in
  2. ctx:claims/beam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5a92a7f8-dbf8-4e2c-bec0-f0a72a9230c9
      Show excerpt
      from concurrent.futures import ThreadPoolExecutor # Create a FAISS index d = 128 # dimension index = faiss.IndexFlatL2(d) # Add vectors to the index vectors = np.random.rand(10000, d).astype('float32') index.add(vectors) # Function to p
  3. ctx:claims/beam/dad116a3-2105-43a3-93d8-198911a2b349
    • full textbeam-chunk
      text/plain1 KBdoc:beam/dad116a3-2105-43a3-93d8-198911a2b349
      Show excerpt
      futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results ``` #### 5. Batch Processing Process queries in

See also

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