Dontopedia

table

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

table has 8 facts recorded in Dontopedia across 2 references, with 3 live disagreements.

8 facts·4 predicates·2 sources·3 in dispute

Mostly:rdf:type(2), has column name(2), column type(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (8)

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.

operatesOnOperates on(2)

storedInStored in(2)

appliedToApplied to(1)

confirmsProperMappingConfirms Proper Mapping(1)

createsEntityCreates Entity(1)

queryOperatesOnQuery Operates on(1)

Other facts (7)

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.

7 facts
PredicateValueRef
Rdf:typeDatabase Schema[1]
Rdf:typeDatabase Table[2]
Has Column Nameid[2]
Has Column Namename[2]
Column TypeINT[2]
Column TypeVARCHAR(255)[2]
Defines TableRisk Profile Table[1]

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/c613f544-8a83-419c-8698-67fbeea99401
ex:DatabaseSchema
definesTablebeam/c613f544-8a83-419c-8698-67fbeea99401
ex:risk-profile-table
typebeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
ex:DatabaseTable
hasColumnNamebeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
id
columnTypebeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
INT
hasColumnNamebeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
name
columnTypebeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
VARCHAR(255)
labelbeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
table

References (2)

2 references
  1. ctx:claims/beam/c613f544-8a83-419c-8698-67fbeea99401
    • full textbeam-chunk
      text/plain1 KBdoc:beam/c613f544-8a83-419c-8698-67fbeea99401
      Show excerpt
      Create a system to track the status of each risk and generate reports. Here's an example using Python and a simple SQLite database: ```python import sqlite3 from datetime import datetime # Connect to the SQLite database conn = sqlite3.con
  2. ctx:claims/beam/f8f42f6b-a669-4fde-b310-665b40c0f92a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f8f42f6b-a669-4fde-b310-665b40c0f92a
      Show excerpt
      {'id': 2, 'name': 'Jane Doe'}, {'id': 3, 'name': 'Bob Smith'} ] # Define the test queries test_queries = [ {'query': 'SELECT * FROM table WHERE name = "John Doe"'}, {'query': 'SELECT * FROM table WHERE id = 1'} ] # Run the

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