I

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-15.)

I has 44 facts recorded in Dontopedia across 17 references, with 6 live disagreements.

44 facts·14 predicates·17 sources·6 in dispute

Mostly:rdf:type(17), represents(5), contains(3)

Rdf:typein dispute`rdf:type`

Anaphor^[1]sourceall time · 2
Output Variable^[2]all time · Af536fe5 Aae4 407e Ad16 72341fd39f7f
Index Array^[3]sourceall time · 9f354551 A9f5 474b A587 082e952c4a41
Indices Array^[4]all time · 49101dfd 4fc4 460c 9cd9 8e0457730c83
Search Result^[5]all time · F262ba02 38a8 487c Ac31 F121b18f4323
Output Array^[6]all time · F5f66e1a 01a9 4eb3 81b7 Fc768e5be38a
Indices Array^[7]all time · C024e566 7bde 4344 Ad2d Cef3f5639007
Indices Array^[8]all time · F026078e 8f4c 49fe 81e1 C274e43d2156
Indices Array^[9]all time · 8c21f541 C703 4998 Aae0 19638ef54326
Indices Array^[10]all time · F1d44342 2a97 4d27 8633 2b8cdeffb413

Inbound mentions (39)

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.

`returns`Returns(7)

Code Snippetex:code-snippet
Index.searchex:index.search
Index.searchex:index.search
Searchex:search
Search Executionex:search-execution
Search Operationex:search-operation
Vector Searchex:vector search

`outputs`Outputs(5)

Printex:print
Printex:print
Print Statementex:print statement
Print Statementex:print-statement
Print Statementex:print-statement

`returnsIndices`Returns Indices(4)

Dense Retrieval Serviceex:dense-retrieval-service
Faiss Index Ivf Pqex:faiss-index-ivf-pq
Nearest Neighbor Searchex:nearest-neighbor-search
Search Operationex:search-operation

`prints`Prints(3)

Faiss Code Snippetex:faiss-code-snippet
Printex:print
Print Iex:print-I

`produces`Produces(2)

Searchex:search
Search Operationex:search_operation

`assignedTo`Assigned to(1)

Search Resultex:search-result

`consistsOf`Consists of(1)

Outputex:output

`contains`Contains(1)

Resultsex:results

`displaysVariable`Displays Variable(1)

Print Outputex:print-output

`ex:outputIndices`Ex:output Indices(1)

Search Operationex:search-operation

`ex:prints`Ex:prints(1)

Print Resultsex:print-results

`ex:returnValues`Ex:return Values(1)

Search Operationex:search-operation

`hasFirstPersonPronoun`Has First Person Pronoun(1)

Speakerex:speaker

`interpolates`Interpolates(1)

Print Statementex:print-statement

`inverseOf`Inverse of(1)

Dex:D

`outputVariables`Output Variables(1)

Search Operationex:search-operation

`returnsMultipleValues`Returns Multiple Values(1)

Faiss Searchex:faiss-search

`returnsPair`Returns Pair(1)

Nearest Neighbor Searchex:nearest-neighbor-search

`returnValues`Return Values(1)

Search Vector Functionex:search-vector-function

`source`Source(1)

Nearest Neighbors Outputex:nearest-neighbors-output

`unpacksSearchResult`Unpacks Search Result(1)

Dense Retrieval Serviceex:dense-retrieval-service

`usesFirstPerson`Uses First Person(1)

Joleneex:jolene

`usesFirstPersonReference`Uses First Person Reference(1)

Speakerex:speaker

Other facts (21)

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.

21 facts

Predicate	Value	Ref
Represents	Neighbor Indices	[8]
Represents	indices	[10]
Represents	indices	[13]
Represents	Indices	[14]
Represents	Indices	[16]
Contains	Vector Indices	[2]
Contains	nearest neighbor indices	[7]
Contains	indices	[10]
Stores	Indices	[5]
Stores	indices	[13]
Is Returned by	Index.search	[7]
Is Returned by	index.search	[12]
Position	1	[2]
Ex:contains	Neighbor Indices	[3]
Printed	true	[6]
Result Type	Indices	[6]
Data Structure	Array	[9]
Returned by	Search	[10]
Inverse of	D	[13]
Data Type	matrix	[14]
Result of	Search	[14]

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.

44 undated

—

typeblah/atlas-ai/2

ex:Anaphor

—

labelblah/atlas-ai/2

—

typebeam/af536fe5-aae4-407e-ad16-72341fd39f7f

ex:OutputVariable

—

containsbeam/af536fe5-aae4-407e-ad16-72341fd39f7f

ex:vector-indices

—

positionbeam/af536fe5-aae4-407e-ad16-72341fd39f7f

—

typebeam/9f354551-a9f5-474b-a587-082e952c4a41

ex:IndexArray

—

containsbeam/9f354551-a9f5-474b-a587-082e952c4a41

ex:neighbor-indices

—

typebeam/49101dfd-4fc4-460c-9cd9-8e0457730c83

ex:IndicesArray

—

typebeam/f262ba02-38a8-487c-ac31-f121b18f4323

ex:SearchResult

—

storesbeam/f262ba02-38a8-487c-ac31-f121b18f4323

ex:indices

—

typebeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

ex:OutputArray

—

printedbeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

true

—

resultTypebeam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a

Indices

—

isReturnedBybeam/c024e566-7bde-4344-ad2d-cef3f5639007

ex:index.search

—

typebeam/c024e566-7bde-4344-ad2d-cef3f5639007

ex:IndicesArray

—

labelbeam/c024e566-7bde-4344-ad2d-cef3f5639007

indices of nearest neighbors

—

containsbeam/c024e566-7bde-4344-ad2d-cef3f5639007

nearest neighbor indices

—

typebeam/f026078e-8f4c-49fe-81e1-c274e43d2156

ex:IndicesArray

—

labelbeam/f026078e-8f4c-49fe-81e1-c274e43d2156

—

representsbeam/f026078e-8f4c-49fe-81e1-c274e43d2156

ex:neighbor-indices

—

typebeam/8c21f541-c703-4998-aae0-19638ef54326

ex:IndicesArray

—

dataStructurebeam/8c21f541-c703-4998-aae0-19638ef54326

ex:Array

—

typebeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

ex:IndicesArray

—

labelbeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

Indices

—

returnedBybeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

ex:search

—

representsbeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

indices

—

containsbeam/f1d44342-2a97-4d27-8633-2b8cdeffb413

indices

—

typebeam/f9316ee6-847e-4064-80dd-6097ca97e0d6

ex:SearchResultArray

—

labelbeam/f9316ee6-847e-4064-80dd-6097ca97e0d6

Indices array

—

isReturnedBybeam/4efeeb64-8572-49af-812f-e5accd46c4ad

index.search

—

typebeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

ex:Array

—

labelbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

—

representsbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

indices

—

storesbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

indices

—

inverseOfbeam/c5e65b2e-6289-4399-808e-64fe4e0eddce

ex:D

—

typebeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

ex:Variable

—

representsbeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

Indices

—

dataTypebeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

matrix

—

resultOfbeam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042

ex:search

—

typebeam/9170f193-72c4-43d3-9c09-87f869d91b8b

ex:Indices-array

—

typebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:IndicesArray

—

representsbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:indices

—

typebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40

ex:Array

—

typelme/a733d4d8-7a5c-46ee-894d-39f976e695ba

ex:FirstPersonPronoun

References (17)

17 references

[1]22 facts
ctx:discord/blah/atlas-ai/2
- full textctx:discord/blah/atlas-ai/2
  text/plain3 KBdoc:discord/blah/atlas-ai/2
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  [2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…
- full textatlas-ai-2
  text/plain3 KBdoc:agent/atlas-ai-2/3a79ad11-fcb3-4da8-b38e-c15390bfab94
  Show excerpt
  [2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…
[2]Af536fe5 Aae4 407e Ad16 72341fd39f7f3 facts
ctx:claims/beam/af536fe5-aae4-407e-ad16-72341fd39f7f
[3]9f354551 A9f5 474b A587 082e952c4a412 facts
ctx:claims/beam/9f354551-a9f5-474b-a587-082e952c4a41
- full textbeam-chunk
  text/plain1 KBdoc:beam/9f354551-a9f5-474b-a587-082e952c4a41
  Show excerpt
  faiss.omp_set_num_threads(4) # Adjust based on your system's capabilities # Create an IVFFlat index quantizer = faiss.IndexFlatL2(128) index = faiss.IndexIVFFlat(quantizer, 128, nlist, faiss.METRIC_L2) # Train the index index.train(vecto…
[4]49101dfd 4fc4 460c 9cd9 8e0457730c831 fact
ctx:claims/beam/49101dfd-4fc4-460c-9cd9-8e0457730c83
- full textbeam-chunk
  text/plain1 KBdoc:beam/49101dfd-4fc4-460c-9cd9-8e0457730c83
  Show excerpt
  - Adjust the search parameters like `efSearch` for `IndexHNSW` to balance between speed and accuracy. ### Example Implementation Here's an optimized version of your code using `IndexIVFPQ` and enabling multi-threading: ```python impor…
[5]F262ba02 38a8 487c Ac31 F121b18f43232 facts
ctx:claims/beam/f262ba02-38a8-487c-ac31-f121b18f4323
[6]F5f66e1a 01a9 4eb3 81b7 Fc768e5be38a3 facts
ctx:claims/beam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a
- full textbeam-chunk
  text/plain1 KBdoc:beam/f5f66e1a-01a9-4eb3-81b7-fc768e5be38a
  Show excerpt
  M = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Search for n…
[7]C024e566 7bde 4344 Ad2d Cef3f56390074 facts
ctx:claims/beam/c024e566-7bde-4344-ad2d-cef3f5639007
- full textbeam-chunk
  text/plain1 KBdoc:beam/c024e566-7bde-4344-ad2d-cef3f5639007
  Show excerpt
  vectors = np.random.rand(100000, 128).astype('float32') # Set the number of threads for parallel processing faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create a…
[8]F026078e 8f4c 49fe 81e1 C274e43d21563 facts
ctx:claims/beam/f026078e-8f4c-49fe-81e1-c274e43d2156
- full textbeam-chunk
  text/plain1006 Bdoc:beam/f026078e-8f4c-49fe-81e1-c274e43d2156
  Show excerpt
  By implementing these optimizations, you should be able to achieve a significant improvement in your dense search goals. [Turn 6398] User: I'm trying to map 3 dense search hurdles with Kathryn for future iterations, and I was wondering if …
[9]8c21f541 C703 4998 Aae0 19638ef543262 facts
ctx:claims/beam/8c21f541-c703-4998-aae0-19638ef54326
- full textbeam-chunk
  text/plain1 KBdoc:beam/8c21f541-c703-4998-aae0-19638ef54326
  Show excerpt
  faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create an IVFPQ index nlist = 100 # Number of clusters M = 8 # Number of sub-quantizers nbits = 8 # Number of bits…
[10]F1d44342 2a97 4d27 8633 2b8cdeffb4135 facts
ctx:claims/beam/f1d44342-2a97-4d27-8633-2b8cdeffb413
- full textbeam-chunk
  text/plain1 KBdoc:beam/f1d44342-2a97-4d27-8633-2b8cdeffb413
  Show excerpt
  M = 8 # Number of sub-quantizers nbits = 8 # Number of bits per sub-quantizer index = faiss.IndexIVFPQ(quantizer, 128, nlist, M, nbits) try: # Train the index index.train(vectors) except Exception as e: logging.error(f"Error …
[11]F9316ee6 847e 4064 80dd 6097ca97e0d62 facts
ctx:claims/beam/f9316ee6-847e-4064-80dd-6097ca97e0d6
- full textbeam-chunk
  text/plain1 KBdoc:beam/f9316ee6-847e-4064-80dd-6097ca97e0d6
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  - **Logging**: Use structured logging (e.g., JSON) and forward logs to a centralized logging system like ELK Stack or Grafana Cloud. ### Step 3: Implementation Details #### Load Balancer Configuration - **Nginx Example**: ```nginx h…
[12]4efeeb64 8572 49af 812f E5accd46c4ad1 fact
ctx:claims/beam/4efeeb64-8572-49af-812f-e5accd46c4ad
- full textbeam-chunk
  text/plain1 KBdoc:beam/4efeeb64-8572-49af-812f-e5accd46c4ad
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  query_vector = np.random.rand(1, 128).astype("float32") # Search for nearest neighbors k = 10 # number of nearest neighbors to retrieve D, I = index.search(query_vector, k) # Print the results print("Distances:", D) print("Indices:", I) …
[13]C5e65b2e 6289 4399 808e 64fe4e0eddce5 facts
ctx:claims/beam/c5e65b2e-6289-4399-808e-64fe4e0eddce
- full textbeam-chunk
  text/plain1 KBdoc:beam/c5e65b2e-6289-4399-808e-64fe4e0eddce
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  m = 8 # number of subquantizers index = faiss.IndexIVFPQ(faiss.MetricType.L2, d, nlist, m, 8) # Train the index index.train(embeddings) # Add the embeddings to the index index.add(embeddings) # Generate a query embedding in a different …
[14]3aa97b5d 2401 4a53 A5d0 4cd1d9b8e0424 facts
ctx:claims/beam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042
[15]9170f193 72c4 43d3 9c09 87f869d91b8b1 fact
ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b
- full textbeam-chunk
  text/plain1 KBdoc:beam/9170f193-72c4-43d3-9c09-87f869d91b8b
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  index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran…
[16]2fcc4e7a D497 4bfa B889 84fb8a9dfe403 facts
ctx:claims/beam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
[17]A733d4d8 7a5c 46ee 894d 39f976e695ba1 fact
ctx:claims/lme/a733d4d8-7a5c-46ee-894d-39f976e695ba
- full textbeam-chunk
  text/plain49 Bdoc:beam/a733d4d8-7a5c-46ee-894d-39f976e695ba
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  My favorite color is teal and I work as a welder.…

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.

I

Rdf:typein disputerdf:type

Inbound mentions (39)

returnsReturns(7)

outputsOutputs(5)

returnsIndicesReturns Indices(4)

printsPrints(3)

producesProduces(2)

assignedToAssigned to(1)

consistsOfConsists of(1)

containsContains(1)

displaysVariableDisplays Variable(1)

ex:outputIndicesEx:output Indices(1)

ex:printsEx:prints(1)

ex:returnValuesEx:return Values(1)

hasFirstPersonPronounHas First Person Pronoun(1)

interpolatesInterpolates(1)

inverseOfInverse of(1)

outputVariablesOutput Variables(1)

returnsMultipleValuesReturns Multiple Values(1)

returnsPairReturns Pair(1)

returnValuesReturn Values(1)

sourceSource(1)

unpacksSearchResultUnpacks Search Result(1)

usesFirstPersonUses First Person(1)

usesFirstPersonReferenceUses First Person Reference(1)

Other facts (21)

Timeline

References (17)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`returns`Returns(7)

`outputs`Outputs(5)

`returnsIndices`Returns Indices(4)

`prints`Prints(3)

`produces`Produces(2)

`assignedTo`Assigned to(1)

`consistsOf`Consists of(1)

`contains`Contains(1)

`displaysVariable`Displays Variable(1)

`ex:outputIndices`Ex:output Indices(1)

`ex:prints`Ex:prints(1)

`ex:returnValues`Ex:return Values(1)

`hasFirstPersonPronoun`Has First Person Pronoun(1)

`interpolates`Interpolates(1)

`inverseOf`Inverse of(1)

`outputVariables`Output Variables(1)

`returnsMultipleValues`Returns Multiple Values(1)

`returnsPair`Returns Pair(1)

`returnValues`Return Values(1)

`source`Source(1)

`unpacksSearchResult`Unpacks Search Result(1)

`usesFirstPerson`Uses First Person(1)

`usesFirstPersonReference`Uses First Person Reference(1)