{
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 "addresses": [
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 "extracted": "2026-11-01",
 "extractor": "Claude (Opus 5.5), subagent, 2026-11-01, one extractor (unaudited, §3.8)",
 "rule": "v0.10 §3.5, source-neutral: opening or abstract, defined terms, headline claims, stated limits; quotes verbatim from the saved page texts (whitespace-normalised for the check; multi-part quotes joined by ' … ', each part checked), the Wikipedia rendered text or wikitext, the paper's pdftotext, or the card's title and snippet",
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 "lineage_note": "Lineages by sense; the field and the archive share them where an archive claim states the same sense. B9 is the same work as B5 and its abstract one lineage with B5 (§3.6). See row.objects.P.rail for each lineage with its earliest source.",
 "pages": "saved files under datasets/negative-of-the-negative/v2/intake/the-pile-20261101/ (Bn.html raw, Bn.txt text extracted with scripts and styles stripped; B2.wiki the raw wikitext; B5.pdf with B5.txt by pdftotext; B1-census.* and B2-census.* the census's saved copies; B9 and B10 the census's saved copies; B6-try.html YouTube's 429 page; tries/ the raw first fetches)",
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  "fetched": 8,
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  "claims_B_D": 74,
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   "origin": "card",
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   "fetched": "card; B8.html sha256 8bbceafb5a18cfa4, HTTP 200 at 2026-11-01T15:13:04Z",
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   "origin": "own_page",
   "pool": "B_R"
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   "fetched": "census third party; B10.html sha256 0a499acc8164a2d2, HTTP 200 at 2026-11-01T05:07:12Z; the census's saved copy, sha256 matching the manifest",
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   "B5",
   "B6",
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  "own_pages": [
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  ],
  "own_pages_note": "the census's own pages: the-pile--home (https://pile.eleuther.ai/) is card 1, carried once, as a card, in B_D (B1); the-pile--paper (the arXiv abstract page) is not on T's rail and stands in B_R (B9); T's card 5 is the paper's PDF on the Pile's host (B5)",
  "wikipedia": [
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  "census_third_party": [
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  "title_only": [
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   "B6"
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    "B6",
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    "kind": "Overview card (the Pile's own home page); the census's own page the-pile--home",
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    "names_operator": "yes ('EleutherAI 2021' in the footer; the citation lists Gao, Biderman and ten others)",
    "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, traininglayerliterature, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title or snippet)",
    "note": "URL from the card's site, title and snippet (the snippet is the page's opening text, verbatim). Refetched with the census agent string; the bytes match the census's saved copy.",
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    "url": "https://en.wikipedia.org/wiki/The_Pile_(dataset)",
    "kind": "Overview card (the Wikipedia article); the entity's Wikipedia article",
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      "tries": [
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        "note": "Wikimedia rate-limit error page"
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       }
      ],
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      "agrees": "one byte longer in this fetch (per-request tokens); the article text the same, last edited 20 August 2026 in both"
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    },
    "names_operator": "yes ('It was constructed by EleutherAI in 2020')",
    "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, traininglayerliterature, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title or snippet)",
    "note": "Article 'The Pile (dataset)', no redirect ('This article is about the dataset. For other uses, see Pile (disambiguation).'); rendered page HTTP 200; raw wikitext HTTP 429 on the first request (a rate-limit page), HTTP 200 on one retry with the same agent string 45 seconds later; last edited 20 August 2026.",
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    "pool": "B_D",
    "role": "Overview card 3",
    "card": "Hugging Face — EleutherAI/pile · Datasets at Hugging Face",
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    "kind": "Overview card (the dataset's repository page and dataset card on Hugging Face)",
    "files": {
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    "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, traininglayerliterature, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title or snippet)",
    "note": "URL by the card's title, which is the page's <title> verbatim. The snippet ('curated by Leo Gao and Stella Biderman ... 100B<1T tokens') is a card-renderer summary of the page's metadata.",
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    ],
    "card_title": "EleutherAI/pile · Datasets at Hugging Face",
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   },
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    "origin": "card",
    "pool": "B_D",
    "role": "Overview card 4",
    "card": "Reddit·r/MachineLearning — [R] The Pile: An 800GB Dataset of Diverse Text for Language Modeling - Reddit",
    "url": null,
    "kind": "Overview card (a Reddit r/MachineLearning post, dated January 1, 2021 on the card)",
    "files": {},
    "fetched": {
     "result": "unreachable: the post's URL could not be determined from the card; three WebSearch queries on the exact title (one restricted to reddit.com, which the search proxy rejected with HTTP 400) returned no Reddit URL; no fetch made. The card's title and snippet are its only text (§3.1(a))",
     "http": null
    },
    "names_operator": "card snippet: yes ('from EleutherAI')",
    "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, traininglayerliterature, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title or snippet)",
    "searches": [
     "WebSearch 'reddit r/MachineLearning \"[R] The Pile: An 800GB Dataset of Diverse Text for Language Modeling\"'",
     "WebSearch (allowed_domains reddit.com) 'The Pile 800GB Dataset of Diverse Text for Language Modeling r/MachineLearning' (proxy HTTP 400)",
     "WebSearch 'reddit MachineLearning \"[R] The Pile: An 800GB Dataset of Diverse Text for Language Modeling\" comments'"
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    "surfaced_by_T": [
     {
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    ],
    "card_title": "[R] The Pile: An 800GB Dataset of Diverse Text for Language Modeling - Reddit",
    "card_snippet": "January 1, 2021 — The Pile Dataset: The Pile is an 800GB publicly available English text corpus from EleutherAI, designed for training large-scale language models, featuring 22 d..."
   },
   {
    "id": "B5",
    "origin": "card",
    "pool": "B_D",
    "role": "Overview card 5",
    "card": "The Pile — The Pile: An 800GB Dataset of Diverse Text for Language Modeling",
    "url": "https://pile.eleuther.ai/paper.pdf",
    "kind": "Overview card (the paper, Gao et al., arXiv:2101.00027, as a PDF on the Pile's own host)",
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    },
    "fetched": {
     "at": "2026-11-01T15:12:23Z",
     "http": 200,
     "url": "https://pile.eleuther.ai/paper.pdf",
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    "names_operator": "yes (authors 'Leo Gao ... Connor Leahy', 'EleutherAI', 'contact@eleuther.ai')",
    "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, traininglayerliterature, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title or snippet)",
    "note": "The card's site is the Pile's own host and its title the paper's; the snippet's '825.18 GiB English text corpus' and 'Composition: 14 ne[w]' match the paper's introduction and contribution 2. URL by the site and title, confirmed by WebSearch (pile.eleuther.ai/paper.pdf). Text extracted with pdftotext (reading order); two-column passages are quoted only where the extraction keeps them contiguous.",
    "searches": [
     "WebSearch '\"The Pile - An 800GB Dataset for Language Models\" \"Paper Read #1\" The Polymath youtube' (returned pile.eleuther.ai/paper.pdf)"
    ],
    "surfaced_by_T": [
     {
      "slug": "the-pile-dataset-aio-20261101",
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    ],
    "card_title": "The Pile: An 800GB Dataset of Diverse Text for Language Modeling",
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   },
   {
    "id": "B6",
    "origin": "card",
    "pool": "B_D",
    "role": "Overview card 6 (video)",
    "card": "YouTube·The Polymath — The Pile - An 800GB Dataset for Language Models - [Paper Read #1]",
    "url": "https://www.youtube.com/watch?v=00ROgQUBvxw",
    "kind": "Overview card (a 35-minute video reading of the paper; no snippet on the card)",
    "files": {
     "B6-try.html": {
      "path": "datasets/negative-of-the-negative/v2/intake/the-pile-20261101/B6-try.html",
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    "fetched": {
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     "http": 429,
     "url": "https://www.youtube.com/watch?v=00ROgQUBvxw",
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     "result": "unreachable: HTTP 429 (YouTube rate limit) on one attempt with the census agent string; no retry by another agent string or route; no transcript fetched. Admitted through its title (the card carries no snippet), §3.1(a)"
    },
    "names_operator": "no (title only)",
    "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, traininglayerliterature, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title or snippet)",
    "searches": [
     "WebSearch (allowed_domains youtube.com) 'The Pile 800GB Dataset for Language Models Paper Read #1 Polymath' (the first result, the exact title)"
    ],
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    "card_title": "The Pile - An 800GB Dataset for Language Models - [Paper Read #1]",
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   },
   {
    "id": "B7",
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   "quote": "Books3 is a dataset of books derived from a copy of the contents of the Bibliotik private tracker made available by Shawn Presser (Presser, 2020). Bibliotik consists of a mix of fiction and nonfiction books and is almost an order of magnitude … We included Bibliotik because books are invaluable for long-range context modeling research and coherent storytelling.",
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   "locus": "§2.12 Project Gutenberg",
   "quote": "Project Gutenberg is a dataset of classic Western literature. The specific Project Gutenberg derived dataset we used, PG-19, consists of Project Gutenberg books from before 1919 (Rae et al., 2019), which represent distinct styles from the more modern Books3 and BookCorpus.",
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   "locus": "§2 Pile-CC",
   "quote": "Our Common Crawl-based dataset, Pile-CC, uses jusText (Endrédy and Novák, 2013) on Web Archive files (raw HTTP responses including page HTML) for extraction, which yields higher quality output than directly using the WET files (extracted plaintext).",
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   "locus": "§2 The Pile Datasets",
   "quote": "we increase the weights of higher quality components, with certain high-quality datasets such as Wikipedia being seen up to 3 times (“epochs”) … 825.18 GiB … 1254.20 GiB",
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   "locus": "Abstract",
   "quote": "Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations.",
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   "locus": "§3 Benchmarking",
   "quote": "Our preferred metric is bits per UTF-8 encoded byte (BPB).",
   "claim": "its metric: bits per UTF-8 encoded byte",
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   "locus": "§3 splits",
   "quote": "it is still possible that some documents are duplicated across the train/validation/test splits.",
   "claim": "some documents may be duplicated across splits",
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   "quote": "Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction.",
   "claim": "an exploratory analysis documents concerning aspects of the data; the construction code is public",
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   "locus": "§6.5 Author Consent and Public Data",
   "quote": "Although one is typically not legally obligated to receive the permission of an author to train a NLP algorithm on their work",
   "claim": "no legal obligation, typically, to obtain an author's permission to train on the work",
   "modality": "interpretation",
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   "quote": "Data with authorial consent is data for which the original authors of the work consented to the use of their data, or where a reasonable person could not assume that their data would not be used for purposes such as research.",
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   "quote": "noncommercial, not-for-profit use of copyright media is preemptively fair use. Additionally, our use is transformative, in the sense that the original form of the data is ineffective for our purposes and our form of the data is ineffective for the purposes of the original documents.",
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   "quote": "Unfortunately, we do not have the metadata necessary to determine exactly which texts are copyrighted, and so this can only be undertaken at the component level. Thus, this should be be taken to be a heuristic rather than a precise determination.",
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    ],
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   "quote": "[Submitted on 31 Dec 2020] … by Leo Gao and 11 other authors",
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   "quote": "an 825 GiB English text corpus targeted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets -- both existing and newly constructed -- many of which derive from academic or professional sources.",
   "claim": "the abstract: an 825 GiB English corpus of 22 subsets, many from academic or professional sources",
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  },
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   "quote": "Using our tools, we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack). We show that our solution enables answering questions about test set leakage and model plagiarism.",
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  }
 },
 "field_sources": {
  "B1": "The Pile — The Pile",
  "B2": "Wikipedia — The Pile (dataset) - Wikipedia",
  "B3": "Hugging Face — EleutherAI/pile · Datasets at Hugging Face",
  "B4": "Reddit·r/MachineLearning — [R] The Pile: An 800GB Dataset of Diverse Text for Language Modeling - Reddit",
  "B5": "The Pile — The Pile: An 800GB Dataset of Diverse Text for Language Modeling",
  "B6": "YouTube·The Polymath — The Pile - An 800GB Dataset for Language Models - [Paper Read #1]",
  "B7": "Hugging Face — The Common Pile v0.1 - Hugging Face",
  "B8": "Michael Brenndoerfer — The Pile: Open-Source Training Dataset for LLMs - Interactive",
  "B9": "arXiv — The Pile: An 800GB Dataset of Diverse Text for Language Modeling (abstract page; census source)",
  "B10": "arXiv — Data Portraits: Recording Foundation Model Training Data (census source)"
 }
}