corpus

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

corpus has 50 facts recorded in Dontopedia across 20 references, with 5 live disagreements.

50 facts·33 predicates·20 sources·5 in dispute

Mostly:rdf:type(8), contains(3), has tokens(3)

Inbound mentions (11)

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.

`usesArgument`Uses Argument(4)

Hdp Modelex:hdp_model
Hdp Model Instantiationex:hdp-model-instantiation
Lsi Modelex:lsi_model
Lsi Model Instantiationex:lsi-model-instantiation

`divides`Divides(1)

Stratified Samplingex:stratified-sampling

`hasNewlinesStripped`Has Newlines Stripped(1)

Python Tutorial Fragmentsex:python-tutorial-fragments

`initializedWith`Initialized With(1)

Bm25 Variableex:bm25-variable

`isInitializedWith`Is Initialized With(1)

Bm25 Variableex:bm25-variable

`isMostlyDueTo`Is Mostly Due to(1)

Bpb Deltaex:bpb-delta

`providesStatusUpdate`Provides Status Update(1)

Lisamegawattsex:lisamegawatts

`takesInput`Takes Input(1)

Analyze Corpusex:analyze-corpus

Other facts (45)

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.

45 facts

Predicate	Value	Ref
Rdf:type	Python Variable	[13]
Rdf:type	Dataset	[13]
Rdf:type	Dataset	[15]
Rdf:type	Dataset	[16]
Rdf:type	Text Dataset	[17]
Rdf:type	Data Set	[18]
Rdf:type	Data Structure	[19]
Rdf:type	List	[20]
Contains	Python Tutorial Fragments	[2]
Contains	Python tutorial fragments	[15]
Contains	Token Lists	[20]
Has Tokens	35.6M	[4]
Has Tokens	133000000	[5]
Has Tokens	133000000	[6]
Prerequisite for	Lsi Model	[13]
Prerequisite for	Hdp Model	[13]
Normalized to	26 letters, space, and punctuation for total 38	[1]
Format Issue	Newlines Stripped	[2]
Trains Model	Model	[2]
Presupposes Mixture	Wiki Philosophy	[3]
Contains Mix	Wikipedia Style Text	[3]
Has Sequences	332882	[4]
Token Count	133M	[5]
Has Size Tokens	133000000	[6]
Divided by Batch Size	4096	[6]
Lacks Eot	null	[7]
Is Raw Text	concatenated	[7]
Probably Is More Complex	Doremi Setup	[8]
Is Just Played Through	Streaming	[8]
Is More Complex Than	Doremi Setup	[8]
Is Not Ordered	Or Not Ordering	[8]
Much Larger Than Batch	54x	[9]
Associated With Repo	Monumentalsystems Chinchilla Master Corpus V1	[10]
Is Still Building	true	[10]
Requires Building	true	[10]
Lacks	Clean Death Record Claim	[11]
Has Biographical Detail on	Jessie Mossman Bawanya	[11]
As of Date	2026-05-31	[12]
Growth Status	growing	[12]
Is Divided by	Stratified Sampling	[14]
Has Sequence Count	332882	[16]
Has Token Count	35600000	[16]
Composition	raw text concatenated	[17]
Is Derived From	Train Df Tokens	[20]
Consists of	Tokenized Documents	[20]

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.

50 undated

—

normalizedToblah/katbot/part-3

26 letters, space, and punctuation for total 38

—

containsblah/safiersemantics/part-79

ex:python-tutorial-fragments

—

formatIssueblah/safiersemantics/part-79

ex:newlines-stripped

—

trainsModelblah/safiersemantics/part-79

ex:model

—

presupposesMixtureblah/watt-activation/part-32

ex:wiki-philosophy

—

containsMixblah/watt-activation/part-32

ex:wikipedia-style-text

—

hasTokensblah/watt-activation/part-84

35.6M

—

hasSequencesblah/watt-activation/part-84

332882

—

tokenCountblah/watt-activation/part-89

133M

—

hasTokensblah/watt-activation/part-89

133000000

—

hasSizeTokensblah/watt-activation/part-91

133000000

—

hasTokensblah/watt-activation/part-91

133000000

—

dividedByBatchSizeblah/watt-activation/part-91

4096

—

lacksEotblah/watt-activation/part-129

null

—

isRawTextblah/watt-activation/part-129

concatenated

—

probablyIsMoreComplexblah/watt-activation/part-695

ex:doremi-setup

—

isJustPlayedThroughblah/watt-activation/part-695

ex:streaming

—

isMoreComplexThanblah/watt-activation/part-695

ex:doremi-setup

—

isNotOrderedblah/watt-activation/part-695

ex:or-not-ordering

—

muchLargerThanBatchblah/watt-activation/part-702

ex:54x

—

associatedWithRepoblah/watt-activation/part-703

ex:monumentalsystems-chinchilla-master-corpus-v1

—

isStillBuildingblah/watt-activation/part-703

true

—

requiresBuildingblah/watt-activation/part-703

true

—

lacksbennet-walker-2026-05-20

ex:clean-death-record-claim

—

hasBiographicalDetailOnbennet-walker-2026-05-20

ex:jessie-mossman-bawanya

—

asOfDateval-mauritius/dossier-FAMILY-INDEX

2026-05-31

—

growthStatusval-mauritius/dossier-FAMILY-INDEX

growing

—

typebeam/29eb6045-85ca-4c16-aabb-7adceec47390

ex:PythonVariable

—

labelbeam/29eb6045-85ca-4c16-aabb-7adceec47390

corpus

—

prerequisiteForbeam/29eb6045-85ca-4c16-aabb-7adceec47390

ex:LsiModel

—

prerequisiteForbeam/29eb6045-85ca-4c16-aabb-7adceec47390

ex:HdpModel

—

typebeam/29eb6045-85ca-4c16-aabb-7adceec47390

ex:Dataset

—

isDividedBybeam/45af0c7a-a92b-45bf-b1f4-496260d16f7b

ex:stratified-sampling

—

typeblah/safiersemantics/77

ex:Dataset

—

labelblah/safiersemantics/77

corpus

—

containsblah/safiersemantics/77

Python tutorial fragments

—

typeblah/watt-activation/84

ex:Dataset

—

labelblah/watt-activation/84

philosophy corpus

—

hasSequenceCountblah/watt-activation/84

332882

—

hasTokenCountblah/watt-activation/84

35600000

—

typeblah/watt-activation/129

ex:TextDataset

—

compositionblah/watt-activation/129

raw text concatenated

—

typebeam/19298204-c17d-4ff3-9158-f6e8c9bd1fa6

ex:DataSet

—

labelbeam/19298204-c17d-4ff3-9158-f6e8c9bd1fa6

Document Corpus

—

typebeam/fac7b295-c13f-4a70-a0ab-5144053a3215

ex:DataStructure

—

labelbeam/fac7b295-c13f-4a70-a0ab-5144053a3215

corpus

—

typebeam/46068d53-96d3-4709-a18e-0c4041019936

ex:List

—

isDerivedFrombeam/46068d53-96d3-4709-a18e-0c4041019936

ex:train-df-tokens

—

consistsOfbeam/46068d53-96d3-4709-a18e-0c4041019936

ex:tokenized-documents

—

containsbeam/46068d53-96d3-4709-a18e-0c4041019936

ex:token-lists

References (20)

20 references

[1]Part 31 fact
ctx:discord/blah/katbot/part-3
[2]Part 793 facts
ctx:discord/blah/safiersemantics/part-79
[3]Part 322 facts
ctx:discord/blah/watt-activation/part-32
[4]Part 842 facts
ctx:discord/blah/watt-activation/part-84
[5]Part 892 facts
ctx:discord/blah/watt-activation/part-89
[6]Part 913 facts
ctx:discord/blah/watt-activation/part-91
[7]Part 1292 facts
ctx:discord/blah/watt-activation/part-129
[8]Part 6954 facts
ctx:discord/blah/watt-activation/part-695
[9]Part 7021 fact
ctx:discord/blah/watt-activation/part-702
[10]Part 7033 facts
ctx:discord/blah/watt-activation/part-703
[11]Bennet Walker 2026 05 202 facts
ctx:genes/bennet-walker-2026-05-20
[12]Dossier Family Index2 facts
ctx:genes/val-mauritius/dossier-FAMILY-INDEX
[13]29eb6045 85ca 4c16 Aabb 7adceec473905 facts
ctx:claims/beam/29eb6045-85ca-4c16-aabb-7adceec47390
- full textbeam-chunk
  text/plain1 KBdoc:beam/29eb6045-85ca-4c16-aabb-7adceec47390
  Show excerpt
  from gensim.models import LsiModel, HdpModel # Perform LSI lsi_model = LsiModel(corpus, num_topics=5, id2word=dictionary) # Print the topics topics = lsi_model.print_topics() print(topics) # Perform HDP hdp_model = HdpModel(corpus, id2wo…
[14]45af0c7a A92b 45bf B1f4 496260d16f7b1 fact
ctx:claims/beam/45af0c7a-a92b-45bf-b1f4-496260d16f7b
- full textbeam-chunk
  text/plain1 KBdoc:beam/45af0c7a-a92b-45bf-b1f4-496260d16f7b
  Show excerpt
  By using stratified sampling and weighted sampling, you can account for the variability in document sizes and improve the accuracy of your volume estimation. This approach ensures that each type of document is adequately represented in the …
[15]773 facts
ctx:discord/blah/safiersemantics/77
- full textsafiersemantics-77
  text/plain3 KBdoc:agent/safiersemantics-77/44c2a4ed-2103-4ae6-a8d3-39339a1ed0c3
  Show excerpt
  [2026-04-29 01:32] xenonfun: last I saw was 32GB of swap and the server isn't responding but proof of concept works [2026-04-29 02:07] xenonfun: private repo runs showing in ci, tho now gotta get them working correct (files: Screenshot_2026…
[16]844 facts
ctx:discord/blah/watt-activation/84
- full textwatt-activation-84
  text/plain3 KBdoc:agent/watt-activation-84/16e41088-c84d-4a6f-9c2d-56d69830cfa6
  Show excerpt
  [2026-03-07 20:41] xenonfun: okay some instant issues with this much data: ``` The problem: mx.eval(loss, model.parameters(), optimizer.state) traverses the full tree of 113M params + Adam's 2x state every step. For the compiled path, mx.ev…
[17]1292 facts
ctx:discord/blah/watt-activation/129
- full textwatt-activation-129
  text/plain3 KBdoc:agent/watt-activation-129/64745479-5d89-4d07-a9b4-ab8506f11ac1
  Show excerpt
  [2026-03-09 04:37] xenonfun: Prompt: 'The theory of' ──────────────────────────────────────────────────────────── The theory of the United States. The American 5th century that was also be seen to bring on 3,000th century. We were 1 in 1956…
[18]19298204 C17d 4ff3 9158 F6e8c9bd1fa62 facts
ctx:claims/beam/19298204-c17d-4ff3-9158-f6e8c9bd1fa6
- full textbeam-chunk
  text/plain1 KBdoc:beam/19298204-c17d-4ff3-9158-f6e8c9bd1fa6
  Show excerpt
  3. **Adjust based on observed performance**: - Increase shards if you need to distribute data more evenly. - Increase replicas if you need to distribute read load or improve fault tolerance. 4. **Test changes incrementally** to ensure…
[19]Fac7b295 C13f 4a70 A0ab 5144053a32152 facts
ctx:claims/beam/fac7b295-c13f-4a70-a0ab-5144053a3215
- full textbeam-chunk
  text/plain1 KBdoc:beam/fac7b295-c13f-4a70-a0ab-5144053a3215
  Show excerpt
  ### Step-by-Step Script 1. **Install Required Libraries**: Ensure you have the necessary libraries installed: ```sh pip install pandas elasticsearch ``` 2. **Script to Analyze Corpus and Integrate with Elasticsearch**: ```pyt…
[20]46068d53 96d3 4709 A18e 0c40410199364 facts
ctx:claims/beam/46068d53-96d3-4709-a18e-0c4041019936
- full textbeam-chunk
  text/plain1 KBdoc:beam/46068d53-96d3-4709-a18e-0c4041019936
  Show excerpt
  ### Step 2: Modify the Code to Use BM25 Here's an example of how you can integrate BM25 into your proof of concept: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import recall_scor…

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corpus

Inbound mentions (11)

usesArgumentUses Argument(4)

dividesDivides(1)

hasNewlinesStrippedHas Newlines Stripped(1)

initializedWithInitialized With(1)

isInitializedWithIs Initialized With(1)

isMostlyDueToIs Mostly Due to(1)

providesStatusUpdateProvides Status Update(1)

takesInputTakes Input(1)

Other facts (45)

Timeline

References (20)

See also

Keep researching

`usesArgument`Uses Argument(4)

`divides`Divides(1)

`hasNewlinesStripped`Has Newlines Stripped(1)

`initializedWith`Initialized With(1)

`isInitializedWith`Is Initialized With(1)

`isMostlyDueTo`Is Mostly Due to(1)

`providesStatusUpdate`Provides Status Update(1)

`takesInput`Takes Input(1)