Dontopedia

List Indexing

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

List Indexing has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

4 facts·3 predicates·2 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.

usesUses(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:typeData Access Pattern[1]
Rdf:typeIndexing Operation[2]
Applied toSearch Speeds[2]
Uses IndexPercentile Index Calculation[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.

typebeam/401284ac-4b49-4678-a3e2-aa44c5ceacbb
ex:DataAccessPattern
typebeam/096f648d-55d2-45ec-8945-3f23e5f318f9
ex:IndexingOperation
appliedTobeam/096f648d-55d2-45ec-8945-3f23e5f318f9
ex:search_speeds
usesIndexbeam/096f648d-55d2-45ec-8945-3f23e5f318f9
ex:percentile-index-calculation

References (2)

2 references
  1. ctx:claims/beam/401284ac-4b49-4678-a3e2-aa44c5ceacbb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/401284ac-4b49-4678-a3e2-aa44c5ceacbb
      Show excerpt
      print(f"Adjusted nprobe search time: {end_time - start_time:.2f} seconds") ``` By systematically adjusting these parameters, you can find the optimal configuration that balances search speed and accuracy for your application. [Turn 1978]
  2. ctx:claims/beam/096f648d-55d2-45ec-8945-3f23e5f318f9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/096f648d-55d2-45ec-8945-3f23e5f318f9
      Show excerpt
      ss.search(f'search {i}') # get search speeds search_speeds = ss.get_search_speeds() # calculate 90th percentile search_speeds.sort() ninetieth_percentile = search_speeds[int(0.9 * len(search_speeds))] print(ninetieth_percentile) # s

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