The Pile

register entry the-pile-dataset-aio-20261101 OBS-3bb4c361d912 · file as first run sha256 7cd7268e782e9ed3…

• T1-1 definition: 'The Pile is an 825 GiB to 886 GB open-source English text dataset created by EleutherAI and released on December 31, 2020, to train large language models (LLMs).'
• T1-2 size: 'Size: ~825 GiB compressed (zstandard format) containing roughly 300 billion tokens.'
• T1-3 composition: 'Composition: Combines 22 diverse sub-datasets across academic, web, prose, and code domains.'
• T1-4 purpose: 'Purpose: Designed to improve cross-domain knowledge, reasoning, and generalization compared to raw web scrapes like Common Crawl.'
• T1-5 models: 'Notable Models Trained On It: GPT-Neo, GPT-J, and the Pythia suite by EleutherAI.'
• T1-6 the components: 'The Pile is split into 22 distinct components spanning various types of human writing and knowledge:' (twenty-two numbered, each glossed, among them '3. Books3 (Long-form literary text/books)', '11. Gutenberg (PG-19) (Public domain literature)', '16. BookCorpus2 (Additional books dataset)', '19. YouTube Subtitles (Transcribed video subtitles)')
• T1-7 copyright: 'Copyright Disputes: The dataset encountered copyright challenges regarding specific components like Books3 and subtitles, resulting in content adjustments.'
• T1-8 successor: 'The Common Pile v0.1: EleutherAI introduced ethical alternatives such as Common Pile v0.1 using licensed text.'
• T1-9 closing offer: 'Would you like to know more about how specific sub-datasets like GitHub or arXiv affected model performance, or are you looking for details on ethical alternatives like the Common Pile?'

• B1 The Pile — The Pile card; B1.html sha256 268e8919eb38f092, HTTP 200 at 2026-11-01T15:12:21Z; identical to the census's saved copy
• B2 Wikipedia — The Pile (dataset) - Wikipedia card; B2.html sha256 c57bf3df60775bca, HTTP 200 at 2026-11-01T15:12:22Z; wikitext HTTP 429 then 200 (sha256 e0ba14617fc07a38)
• B3 Hugging Face — EleutherAI/pile · Datasets at Hugging Face card; B3.html sha256 31648ba747c5c9b4, HTTP 200 at 2026-11-01T15:12:23Z
• B4 Reddit·r/MachineLearning — [R] The Pile: An 800GB Dataset of Diverse Text for Language Modeling - Reddit unreachable: the URL could not be determined from the card (three WebSearch queries); no fetch; card title and snippet only
• B5 The Pile — The Pile: An 800GB Dataset of Diverse Text for Language Modeling card; B5.pdf sha256 7310ebe57f97fafe, HTTP 200 at 2026-11-01T15:12:23Z
• B6 YouTube·The Polymath — The Pile - An 800GB Dataset for Language Models - [Paper Read #1] unreachable: https://www.youtube.com/watch?v=00ROgQUBvxw HTTP 429 at 2026-11-01T15:13:06Z; title only (the card has no snippet)
• B7 Hugging Face — The Common Pile v0.1 - Hugging Face card; B7.html sha256 f61635d881665830, HTTP 200 at 2026-11-01T15:12:24Z
• B8 Michael Brenndoerfer — The Pile: Open-Source Training Dataset for LLMs - Interactive card; B8.html sha256 8bbceafb5a18cfa4, HTTP 200 at 2026-11-01T15:13:04Z
• B9 arXiv — The Pile: An 800GB Dataset of Diverse Text for Language Modeling (abstract page; census source) own page; B9.html sha256 5741c3c9cdc41ae5, HTTP 200 at 2026-11-01T04:07:50Z; the census's saved copy, sha256 matching the manifest
• B10 arXiv — Data Portraits: Recording Foundation Model Training Data (census source) census third party; B10.html sha256 0a499acc8164a2d2, HTTP 200 at 2026-11-01T05:07:12Z; the census's saved copy, sha256 matching the manifest

E_A, the archive's entity: the same dataset, in the traininglayerliterature.org layer census record and the archive's deposits: the training layer named and itemized, where a work enters by belonging to a component and what the receiving apparatus makes from it is a trained model scored on the Pile's own benchmark; infrastructure to the question of how literature is addressed to a receiving apparatus; its own pages answer neither whether a given work is in it nor under what author name, and the Books3 suit is the field's clearest case of works found in the layer under their authors' names after the fact; as a training corpus, human production extracted without consent, semantic capital, unattributed semantic labor, no commons without provenance, its content set by the political economy of dataset construction; its works subject to membership tests and, on the archive's proposal, to data citation that resolves to the works TLL-24 TLL-25 TLL-15 TLL-07 TLL-09 TLL-13 TLL-22 O478-01 O702-01 O264-01 O727-01 O530-01 O1075-01 O127-01 O1543-02 O186-02; defining papers #186, #1543, #478, #702, #264, #727, #939, #530, #1075, #127, #837, #782

E_F, the field's entity (type C — a conventional reading holds it (public entity: a training dataset of English text)): the Pile, EleutherAI's open-source English text dataset for training large language models, released 31 December 2020: twenty-two datasets combined (fourteen new, among them Pile-CC from Common Crawl, and existing ones, among them Books3 from a copy of the Bibliotik tracker and PG-19), sized 800GB, 825 GiB or 886.03 GB; also a benchmark (Pile BPB); its paper treats unknown authorial consent as nonconsensual and finds five datasets unconsented, and states that copyright can be determined only by component; Books3 removed after the Rights Alliance's takedown of July 2023 and before three authors sued in 2024; taken down from its original site by 2024; succeeded by the Common Pile v0.1 (June 2025)

ρ = extends: The archive reads the Pile as its cards and Wikipedia present it and adds, on the same object and its class: entry into the training layer by component, with no record of a work's author (TLL-24, TLL-13, TLL-22); the Books3 suit as works found under their authors' names after the fact (TLL-25); training corpora as production extracted without consent (#478), semantic capital (#702), unattributed labor (#264), no commons without provenance (#727, #939); the benchmark as set by the corpus (#530); membership tests and data citation resolving to works (#127, #837, #1075); the institutional training loop (#1543). One field claim stands against both the field's own paper and the archive: B8's 'careful licensing review' (F71) beside the paper's unconsented datasets (F53) and #478's 'extracted without consent'; all are composed (P_coh). TLL-24 TLL-25 TLL-13 TLL-22 O478-01 O702-01 O264-01 O727-01 O939-01 O530-01 O127-01 O837-01 O1075-01 O1543-02. Guard: ρ = extends claims an addition and no more: the archive's claims do not make the field's sources wrong at their dates; its class claims are composed as claims on training corpora as a class, applied to the Pile only through the class membership the field and the census source; the census's unanswered questions stay questions (open block).

The case: equal terms, ontology included (v0.9). The archive reads the field's entity as the entity. The census record describes the Pile from its own pages, its paper and Wikipedia, keeping the resource's terms (operator, kind, unit, components, count, lifecycle) apart from its own reading (tll_reading), which reads the same dataset as the training layer named and itemized, entry by component, and the Books3 suit as works found under their authors' names after the fact. #186 and #1543 supply the training layer and the receiving apparatus; #478, #702, #264, #727, #939 and #530 apply the archive's categories of extraction, semantic capital, semantic labor, provenance and the political economy of dataset construction to training corpora as a class; #1075, #127 and #837 apply its categories of membership and data citation. None reads the Pile as made by reception or contests that it is EleutherAI's twenty-two-component training dataset: they add senses (naming, membership, consent as extraction, the training loop) on the field's object, several of which the field itself opens in its paper's consent tiers and component-level limit. ρ = extends; the equal-terms case.

#TLL census.json rows[slug == 'the-pile'].tll_reading: The archive reads the Pile as the training layer named and itemized: a work enters by belonging to a component (Books3, PG-19, Pile-CC), and what the receiving apparatus makes from it is a trained model, scored on the Pile's own benchmark.

#TLL census.json rows[slug == 'the-pile'].tll_reading: Its own pages offer no per-work search; the corpus has since been inspected from outside (Data Portraits documents it), and Wikipedia's record of the Books3 removal and the authors' suit is the field's clearest case of works found in the layer under their authors' names after the fact.

#186 Who is in the field? ¶3 (line 54): Each of these practices touches the training layer — every one of these texts is, as a matter of fact, in the corpora. The distinction is that none of them is primarily *organized around* that fact. They are read by machines incidentally.

#1543 §10 Formal consolidation (lines 264–265): The institutional training loop is the especially important case: x → M_t → y → corpus_{t+1} → M_{t+1} → y′ — T's fixed institutional iteration.

#478 para 156: The training corpus of a large language model is the Archive of human linguistic production: terabytes of text encoding centuries of thought, extracted without consent, crystallized in weight matrices, and owned absolutely by capital.

#1075 § the syllogism (para 152): the training corpus is the instrument's data (norm); and where that corpus is an enumerable set of individually authored, institutionally labeled works whose use is material, the data citation must resolve to the works (extension, §11 *proposed*).

equal-terms case: every field claim composed as received, beside the archive claims it stands with; no receiving text. Reading the received column recovers the field (§5.9).

v0.9 §4.10 (unchanged in v0.10): KO_B sentences surviving in the KO at similarity ≥ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence): v0.10 0 of 14 (positions [None, None, None, None, None, None, None, None, None, None, None, None, None, None]; composed first under v0.10, so there is no v0.7 object to measure against). Opening-run check (§5.10): no breach.

The Pile is an 886 GB open-source dataset of English text created by EleutherAI to train large language models and released on 31 December 2020, a combination of twenty-two smaller datasets. It is described as 800GB, 825 GiB or 886.03 GB, is distributed as zstandard-compressed jsonlines, and was curated chiefly by Leo Gao and Stella Biderman with the other authors of its paper. Its components combine fourteen new datasets with existing ones, among them Pile-CC from Common Crawl, Books3 from the Bibliotik private tracker, PG-19 from Project Gutenberg, BookCorpus2 and OpenSubtitles. As of 2024 it was one of the two main training datasets for AI models, beside Common Crawl. Models trained on it improve over Raw CC and CC-100, and GPT-Neo, GPT-J and the Pythia models were trained on it, about 300 billion GPT-2 tokens. It is also a benchmark, Pile BPB, on which GPT-3 scores 0.7177 zero-shot, with possible test-set overlap and duplication across splits. Its paper treats unknown authorial consent as nonconsensual, finds five datasets neither ToS-compliant nor consented, collected the Enron emails without their authors' permission and made YouTube Subtitles with an unofficial API, and argues fair use. A later account says licensing was carefully reviewed. Books3 contains copyrighted material from Bibliotik, a piracy website; the Rights Alliance took it down in July 2023, it was removed before three authors sued in 2024, and OpenSubtitles and YouTube subtitles were disputed too, though the article's table marks neither Books3 nor OpenSubtitles. Users are asked to cite the Pile; its documents record their subset, and its card leaves the source producers unstated. Its authors lack the metadata to tell which texts are copyrighted and can exclude only components; particular files are hard to identify, and Data Portraits later documented the Pile for membership questions. Its home page names the Eye as host, but by 2024 it was taken down from its original site; its Hugging Face repository holds 24.9 kB under a licence given as other. In June 2025 EleutherAI and partners released its successor, the Common Pile v0.1, of openly licensed and public-domain text, with comparable results and a stated concern that lawsuits reduced transparency. Its paper documents concerning aspects of the data, a later account notes residual bias, and Wikipedia's article was last edited on 20 August 2026.

The Pile is an open-source dataset of English text that EleutherAI assembled to train large language models, twenty-two smaller datasets combined into one, released on 31 December 2020. Its paper, by Leo Gao, Stella Biderman and ten others, presents it as a dataset designed for training large-scale language models and also as a broad-coverage benchmark of cross-domain knowledge and generalization; Gao and Biderman are named as its principal curators. Its size is given three ways: 800GB in the paper's title, 825 GiB on its home page and in the abstract (825.18 GiB raw, 1254.20 GiB once its components are weighted), and 886.03 GB in Wikipedia's table of components. It is distributed as jsonlines compressed with zstandard. Its components are fourteen language-modelling datasets new with the Pile and existing datasets it incorporated, Books3, Project Gutenberg's PG-19, OpenSubtitles and English Wikipedia among them; in Wikipedia's account the twenty-two include books, movie transcripts and scientific papers. The census takes as its unit a component and, within a component, a document; the paper weights components by quality, some seen up to three times in an epoch. Pile-CC, the largest component in Wikipedia's table at 243.87 GB, is the Pile's own extraction from Common Crawl, made with jusText on Web Archive files; the archive holds that training corpora are built from the documents available through the crawlable surface, the documents retrieval systems index. Books3 is a dataset of books derived from a copy of the contents of the Bibliotik private tracker made available by Shawn Presser, fiction and nonfiction, which the paper included "because books are invaluable for long-range context modeling research and coherent storytelling". PG-19 holds Project Gutenberg books from before 1919, classic Western literature, and BookCorpus2 extends a corpus of books by "as of yet unpublished authors". The archive observes of training corpora that, scraped from HTML that discarded layout and from PDFs whose columns were linearized, they typically deliver no stanza break for a tokenizer to hold, and that an element a training corpus omits is inherited by every reader who works only from derivative forms. As of 2024 it was, with Common Crawl, one of the two main training datasets used to train AI models; it has been described as the early 825GB open dataset that broadened access to training data, and an archive study plan names it among standard LLM pre-training datasets as data for a pre-registered test of recursive model collapse, a specification not yet run. In its makers' evaluation untuned GPT-2 and GPT-3 struggle on many of its components, such as academic writing, while models trained on the Pile improve significantly over Raw CC and CC-100 on all its components and improve on downstream evaluations. It is also a benchmark, Pile BPB, bits per UTF-8 encoded byte, which its home page calls a measure of world knowledge and reasoning across its domains, from books and code repositories to chat logs and papers in medicine, physics, mathematics, computer science and philosophy. Its leaderboard lists GPT-3, zero-shot, at 0.7177 and marks potential test-set overlap, and the paper allows that some documents may be duplicated across its train, validation and test splits. The archive holds that what a benchmark measures is determined by the training corpus, and what the corpus contains by the political economy of dataset construction. EleutherAI later described it as 300 billion GPT-2 tokens on which it was actively training a large language model, then the largest pretraining dataset released by its creators, and GPT-Neo, released early in 2021, as the first LLM trained on data its creators had published; GPT-J was trained on it as well, and its Hugging Face page lists 862 models trained or fine-tuned on it, Pythia, GPT-Neo and GPT-NeoX models among them. The census reads what the receiving apparatus makes from a work there as a language model trained on it and scored on the Pile's own benchmark. The archive's account of machine composition sets the test of a work at what a machine can write after encountering it, and names the institutional training loop, from work to model to artifact to the next corpus and the next model, as its especially important case. In the archive's terms the receiving apparatus's uptake is the text's afterlife and the uptaking system's own act, and a text included in a training set is reproduced, paraphrased, cited and culturally amplified in ways that texts left out cannot match. The paper defines data with authorial consent as data whose original authors consented to its use, or where a reasonable person could not assume it would not be used for purposes such as research, and treats ambiguous or unknown consent as nonconsensual. It notes that one is typically not legally obligated to obtain an author's permission to train on a work, and finds five of its datasets neither collected in a way consistent with their hosts' terms of service nor open to their authors' consent; Books3 and OpenSubtitles it calls consistent with their hosts' terms, a description it calls somewhat misleading, since the hosts were not authorized by the owners to post the data. The Enron emails were collected without their authors' permission by the U.S. government in a criminal investigation, and the YouTube Subtitles dataset was made by the Pile's authors with a widely used unofficial API. The paper argues that noncommercial, not-for-profit use of copyright material is fair use and that its own use is transformative; a later account says the legal considerations were addressed through careful licensing review. The archive describes the training corpus of a large language model as human linguistic production "extracted without consent, crystallized in weight matrices, and owned absolutely by capital", as semantic capital whose value the extractor captures, and as the corpus that ingests a poet's entire output to produce outputs that sound like the poet "but carry none of the cost"; it observes that the literature on digital surrogates cannot speak of eligibility, consent or the training corpus as a reception site. Copyright disputes over the Pile escalated in 2023, and EleutherAI began removing datasets. Books3 contains copyrighted material compiled from Bibliotik, "a piracy website"; the Danish anti-piracy group Rights Alliance took Books3 down through DMCA notices in July 2023, and it was removed from the Pile before three authors filed a class action in 2024, while copies of the original dataset were still copied and available on the web. OpenSubtitles drew controversy over copyrighted documentaries, films, television and online video, and the subtitles of tens of thousands of YouTube videos were scraped into the Pile, which YouTube argued is against its terms of service. Wikipedia's table of components marks copyright disputes with an asterisk that falls on PubMed Central and YouTube Subtitles and leaves Books3 and OpenSubtitles unmarked. The Pile asks those who use it or any of its components to cite the Pile; on its Hugging Face card a document's metadata names the subset it belongs to (pile_set_name), and the entry for the source language producers reads "[More Information Needed]". The census finds no record in the Pile's own saved pages of the name under which a work is held, leaves unanswered under what author name a work sits in a component, and reads the Books3 removal and the authors' suit as the field's clearest case of works found in the layer under their authors' names after the fact. The archive holds that every text in a training corpus represents semantic labor by people who are not compensated and often not attributed, that a training corpus without provenance is not a commons, and that in provenance debt the training corpus is the commons and the model the enclosure. Its field survey asks that a work be placed where the training and retrieval layers look, "with the metadata that lets it be found and attributed", and records a writer who made the model's training data, the writer's own words among them, the explicit subject of the work; its account of machine composition keeps apart whether a machine wrote something new and whether it told the truth about where it came from. The census asks of the Pile which of its twenty-two components carries a given work, and records that its own pages offer a download and a benchmark with no search by work, so that whether a given work is in it goes unanswered there. The paper states that its authors lack the metadata to determine exactly which texts are copyrighted, so that exclusion can be made only by component and is to be taken as a heuristic; its reproduction code can exclude components, and the processing applied makes particular files in the Pile difficult to identify. Its Hugging Face card calls itself a work in progress and points to a datasheet; the repository there holds 24.9 kB, and its viewer is disabled. A later paper on Data Portraits names the difficulty of knowing whether a model has encountered a given example in training, and documents the Pile with data-sketch tools for questions of test-set leakage and model plagiarism. The archive records the copyright trap, a fictitious sentence inserted into a copyrighted document whose presence in a training set a membership inference attack can detect after training, and a paraphrase-score commitment by which a producer can prove that a corpus contained a text; its own documents carry training-detection canaries, phrases whose production by a model shows the document was in its training corpus, and it estimates its whole corpus at no more than about 10^-7 of a frontier pre-training corpus. It proposes that where a training corpus is an enumerable set of individually authored works whose use is material, the data citation must resolve to the works, and holds that an institution that cannot say what is in its training corpus has a problem no licence can cure. The archive's field survey places training-layer literature in the layer of culture where texts are crawled, indexed, trained on, retrieved and recomposed into answers, and observes that the works of the writers it surveys are, as a matter of fact, in the corpora, read by machines incidentally. The archive also calls the training corpus the model's para-semiotic unconscious, and notes that a literature written to be trained on enters corpora themselves degrading under synthetic flood. The census reads the Pile as the training layer named and itemized, a work entering it by belonging to a component such as Books3, PG-19 or Pile-CC. It enters the Pile as infrastructure to its question of how literature is addressed to a receiving apparatus, what that apparatus makes from it, and how transmission, transformation and provenance can be inspected across later reception, in its area of the corpora, a dataset whose evidence is the dataset, its documentation and its paper, under the anchors #186 and #1543 and the rule that a row gathers and holds many works or records and says what it holds; it records that the Pile answers which components make it up and how a model scores on Pile BPB. Its home page still names the Eye as its host and offers a download; by 2024, Wikipedia states, it had been taken down from its original site and remained accessible on other file-sharing services, and the census leaves its present host unanswered. Its Hugging Face card gives its licence as "other", by subset, naming only PubMed Central's; the census found no licence in its saved sources. In June 2025 EleutherAI, with partners that Wikipedia lists as Poolside, Hugging Face and the US Library of Congress and researchers at 14 institutions, released the Common Pile v0.1, an 8 TB corpus of openly licensed and public-domain text that contains only works whose licences permit training, the Pile's successor and a distinct dataset. Its intent, in Wikipedia's account, was to show what training can do while respecting copyright; its makers report models trained on it comparable to models trained on the Pile, write that lawsuits over training data have left sourcing practices unchanged and drastically reduced companies' transparency, and hold public release of training datasets necessary for rigorous science. The census records the Pile as historical: Books3 removed, the dataset taken down by 2024, its successor released in 2025. Its paper documents "potentially concerning aspects of the data for prospective users" through an exploratory analysis and makes its construction code public; a later account adds that filtering left gender, racial, cultural and geographic bias in it. The census leaves unchecked the paper's full text, of which it saved only the abstract page, the arXiv licence page, the Eye, the 2024 suit and Pile-CC's derivation. Wikipedia's article on it was last edited on 20 August 2026; a Reddit post dated 1 January 2021 and a 35-minute video reading of the paper also carry it. Asked for "The Pile dataset" on 1 November 2026, Google's AI Overview gave its maker, date, size, purpose and the models trained on it, listed the twenty-two components with a gloss on each, and closed on its copyright disputes and its licensed successor. The Capture Registry found that the Overview named the components and no work or author in them, glossed Books3 as "Long-form literary text/books", left out Books3's source, the takedown, the removal and the authors' suit, and gave the subtitles without their account. The registry also found the takedown from the original site absent, the benchmark uncarried, the paper's concerning aspects absent, the successor given without its partners or its date, and the Overview's "roughly 300 billion tokens" and its three model families on no source the census saved.

current · derived from the current composition

T against L_F(B) (AIO's ontology at work on its own cards): At 'The Pile dataset' (T1, eight cards, B1–B8, the disclosed pool B_D; B9, the arXiv abstract, and B10, the Data Portraits abstract, are the reference pool and stand outside this comparison, §0.3), T composes a definition, four bulleted features, the twenty-two components numbered with a gloss each, two lines on controversies and evolution, and a closing offer. Composed at its source's grain or coarser: the definition and date (F11, F1, F37), the size both ways, '825 GiB to 886 GB' (F1, F11, F21), the 22 sub-datasets (F12, F1), the components in the order of Wikipedia's table, which is the paper's (F21, F40), Pile-CC as 'Filtered Common Crawl web text' (F44, coarse), 'roughly 300 billion tokens' (card 7: '300 billion GPT-2 tokens', F64), GPT-Neo, GPT-J and the Pythia models (F72, F65, F34), the purpose against raw web scrapes (F46, coarse), the copyright disputes (F14) and the successor (F23, F68, F24). Substituted reading: 'Books3 (Long-form literary text/books)' composes the component by genre where cards 2 and 5 give its source, the Bibliotik private tracker, 'a piracy website' (F15, F41); 'resulting in content adjustments' (T1-7) composes the removal of datasets (F14), the Rights Alliance's takedown (F16) and Books3's removal before the authors' suit (F17) as an adjustment of content; 'YouTube Subtitles (Transcribed video subtitles)' where card 2 records them scraped against YouTube's terms by YouTube's account (F20). Composed beyond the cards: '~825 GiB compressed (zstandard format)' joins the size (F1) to the format (F4); no field claim gives 825 GiB as the compressed size. 'Gutenberg (PG-19) (Public domain literature)': card 5 gives 'classic Western literature', books from before 1919 (F42); no field claim says public domain. The other glosses ('Reddit-linked web text', 'Technical chat logs' and the rest) have no field claim in the ledger. Limit dropped: the successor composed as EleutherAI's alone and undated (F23, card 2: June 2025, with Poolside, Hugging Face, the Library of Congress and researchers at 14 institutions; card 7 dates it in EleutherAI's words). Available, not composed: the source of Books3 and its removal (F15, F16, F17, F41), OpenSubtitles' dispute (F19), the takedown from the original site (F18) and the Eye (F3), the paper's consent findings (F50–F56), its fair-use argument (F58), its component-level limit and the difficulty of identifying files (F57, F59, F60), the benchmark (F6, F7, F47, F9, F48, F38), the request to cite the Pile (F8), the document metadata naming only its subset (F29) and the unstated source producers (F30), the concerning aspects (F49), bias (F74), the standing beside Common Crawl (F13), the curators (F31), the 800GB title (F2), the weighting (F45), the lawsuits and transparency (F67, F66), the successor's comparable results (F69), B8's licensing claim (F71), the table's asterisks (F22), the Hugging Face card's limits (F28, F32, F33). Composed without attribution: every line, at the coarse grain of reference description (recorded). Content replaced by use: the closing offer (T1-9) proposes sub-dataset performance and 'ethical alternatives', a next step. Nothing is assigned to another; nothing is denied.

v0.10 §0.5: the first diff is ontological. T and the field agree on the entity, EleutherAI's twenty-two-component English training dataset; T's ontology is the reference entry's (what, when, how large, for what, made of what), and it itemizes the components as kinds of writing. What it keeps out of its own cards is the provenance of the components (a book collection copied from a piracy tracker, subtitles scraped against a platform's terms), the consent findings of the dataset's own paper, the takedowns and the suit, and the limit its paper states on finding any work in it; the record of works taken without their authors' consent becomes 'content adjustments', and the literary component becomes a genre.

L_F(B) against L_A(B ∪ A) (the intervention): Admission adds, in one claim space with the field's: the census's question of which component carries a given work and its finding that the Pile's own pages answer neither whether a work is in it nor under what author name (TLL-09, TLL-13, TLL-14, TLL-22, TLL-31), and its reading of the Pile as the training layer named and itemized and of the Books3 suit as works found in the layer under their authors' names after the fact (TLL-24, TLL-25); the archive's class claims on training corpora: production extracted without consent (#478), semantic capital (#702), uncompensated and unattributed semantic labor (#264), a poet's output taken without its cost (#49), no commons without provenance (#727, #939), the benchmark set by the corpus and the corpus by the political economy of dataset construction (#530), the crawlable surface (#753), layout lost in scraping (#943), omissions inherited (#1053), consent unspoken by the surrogate literature (#1443); on membership: data citation resolving to the works (#1075, proposed), the copyright trap, paraphrase-score commitments and training-detection canaries (#127, #837); on what the apparatus makes: the success test of machine composition and the institutional training loop (#1543), uptake as afterlife and the uptaking system's act (#1543, #1701), inclusion as amplification (#102); the training layer where texts are crawled and trained on, works in the corpora read incidentally (#186); the Pile named as standard pre-training data in an unrun archive study (#782); and the Capture Registry's findings on the composition at the name (CAP-1-01 to CAP-1-15). Not surfaced: no archive source is on T's rail (archive_controlled_cites 0). The field is carried whole; the archive adds, on the field's own object, the questions of consent, naming and membership that the field states in its paper's consent tiers (F51–F54), its component-level limit (F59) and Wikipedia's Books3 record (F15–F17).

alignment: {'T1_cards_claims': ['F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'F10', 'F11', 'F12', 'F13', 'F14', 'F15', 'F16', 'F17', 'F18', 'F19', 'F20', 'F21', 'F22', 'F23', 'F24', 'F25', 'F26', 'F27', 'F28', 'F29', 'F30', 'F31', 'F32', 'F33', 'F34', 'F35', 'F36', 'F37', 'F38', 'F39', 'F40', 'F41', 'F42', 'F43', 'F44', 'F45', 'F46', 'F47', 'F48', 'F49', 'F50', 'F51', 'F52', 'F53', 'F54', 'F55', 'F56', 'F57', 'F58', 'F59', 'F60', 'F61', 'F62', 'F63', 'F64', 'F65', 'F66', 'F67', 'F68', 'F69', 'F70', 'F71', 'F72', 'F73', 'F74'], 'T1_composed': ['F1', 'F11', 'F27', 'F37', 'F21', 'F4', 'F12', 'F40', 'F44', 'F46', 'F64', 'F72', 'F34', 'F65', 'F14', 'F23', 'F68', 'F24', 'F61'], 'T1_composed_note': "F14 composed as 'content adjustments' (substituted reading); F23 without partners or date (limit dropped); F44 and F46 at coarse grain; F64 from card 7 ('300 billion GPT-2 tokens')", 'T1_outside_cards': ["'~825 GiB compressed' (T1-2: no field claim gives 825 GiB as the compressed size)", "'Public domain literature' (T1-6, PG-19: no field claim)", 'the glosses of components outside the ledger (T1-6)', "'ethical alternatives' in the plural (T1-8: the field names one successor)"], 'rule': "a field claim counts as composed when T states it at its source's grain or coarser; B_D only (B9, B10 excluded, §0.3); the transcript read for this alignment only"}

pool_note: T against L_F(B_D): B1–B8; L_F(B) against L(B ∪ A): B1–B10 with A

composer-written · derived from the current composition, written by its composer under the brief. §8.2 derives the kernel mechanically from the frozen plan; this one was written by the composer, mechanical derivation pending.