{
 "row": "ithaca",
 "address": "Ithaca DeepMind",
 "addresses": [
  "Ithaca DeepMind"
 ],
 "extracted": "2026-10-31",
 "extractor": "Claude (Opus 5.5), subagent, one extractor (unaudited, §3.8)",
 "rule": "v0.9 §3.5, source-neutral: opening or abstract, defined terms, headline findings, stated limits; quotes verbatim from the saved page texts (whitespace-normalised for the check; multi-part quotes joined by ' … ', each part checked), the meta description of the JavaScript interface page, the card snippet of the unreachable repository, and the Wikipedia section and disambiguation page; 62/62 verified by script",
 "B_note": "The field B (§0.4, §3.1(a)), by origin: 'card', the four cards on T's rail, all the entity's (B1 the DeepMind post; B2 the GitHub repository, unreachable, its snippet the text; B3 Predicting the Past, the interface, also the census's own page ithaca--home; B4 the Nature paper, also the census's one source ithaca--nature); 'own_page', none outside the rail (both census sources are cards); 'wikipedia', no article, the model described in 'Google DeepMind' › 'Archaeology' and the bare name a disambiguation page (B5). Fetched with the census's agent string, 'alexanarch-seating/1.0 (https://www.alexanarch.org)', 2026-10-31.",
 "lineage_note": "Lineages: what it is: a neural network that restores, places and dates Greek inscriptions (B1, B2, B3, B4, B5); the name: Homer's island (B1, B4, B5); reception: the encyclopedia and the bare name (B5); the makers: DeepMind, its academic partners and the paper's authors (B1, B4); the problem: damaged, moved and undatable inscriptions (B1, B4); the data: PHI and I.PHI (B1, B4); the architecture: characters and words, sparse attention, three heads (B1, B4); the outputs: ranked hypotheses, distributions, saliency (B1, B4); the headline figures: 62%, 71%, within 30 years (B1, B4, B5); the collaboration figure: 25% to 72%, and who the evaluators were (B1, B4, B5); redating Classical Athens (B1, B4); the stated limits: an assistive tool, plausible suggestions, the tradition in the data (B4); access and licences: the interface, the code, the weights, the article (B1, B3, B4); genealogy and successors: Pythia before, other languages and Aeneas after (B1, B4, B5); scope: other ancient texts and disciplines (B4). B1 and B4 are the same work's two first-party instances of 9 March 2022 (the post and the paper), kept as two sources; where they state one claim, both instances are carried in the lineage and the paper is listed first. The census's fields that restate the paper (kind, operator, method, findings, stated_limits) are one lineage with B4 (§3.6).",
 "pages": "saved files under datasets/negative-of-the-negative/v2/intake/ithaca-20261031/ (Bn.html raw, Bn.txt text extracted with scripts and styles stripped; B2-403.html the refusal; B3-meta.txt the interface page's title and meta description; B4-census.html the census's copy of the paper; W1–W14 raw Wikipedia responses, B5.html/B5.txt the rendered 'Google DeepMind' (W13), W7.txt the rendered disambiguation; T1-ithaca-deepmind-aio-20261031.txt the registry's transcript string); sha256 per file in each source's 'files'",
 "field": [
  {
   "id": "B1",
   "origin": "card",
   "role": "Overview card 1",
   "card": "Google DeepMind — Predicting the past with Ithaca — Google DeepMind",
   "url": "https://deepmind.google/discover/blog/predicting-the-past-with-ithaca/",
   "kind": "Overview card (the operator's post of 9 March 2022, by Yannis Assael, Thea Sommerschield, Brendan Shillingford and Nando de Freitas)",
   "files": {
    "B1.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B1.html",
     "sha256": "832d3d6cfd97b40ad266c0fe64f28f9e9e3eabab66f1d73a47403462f5a5ff26"
    },
    "B1.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B1.txt",
     "sha256": "6de295b7663778e51d28c7260a573054deeb3011e09049ba59889c08f10f5292"
    }
   },
   "fetched": {
    "at": "2026-10-31T04:39:12Z",
    "http": 200,
    "url": "https://deepmind.google/discover/blog/predicting-the-past-with-ithaca/",
    "final_url": "https://deepmind.google/blog/predicting-the-past-with-ithaca/",
    "raw": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B1.html",
    "raw_sha256": "832d3d6cfd97b40ad266c0fe64f28f9e9e3eabab66f1d73a47403462f5a5ff26",
    "agent": "alexanarch-seating/1.0 (https://www.alexanarch.org)",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B1.txt"
    ]
   },
   "names_operator": "yes ('DeepMind'; the authors Yannis Assael, Thea Sommerschield, Brendan Shillingford, Nando de Freitas; the partners Ca' Foscari, Oxford, AUEB; the web interface's developers at Google Cloud)",
   "note": "URL by WebSearch on the card's title and site (deepmind.google/discover/blog/…, which redirects to deepmind.google/blog/…); the page's <title> is the card's title. Fetched once with the census's agent string. Not a census source.",
   "surfaced_by_T": [
    {
     "slug": "ithaca-deepmind-aio-20261031",
     "card": 1
    }
   ],
   "card_title": "Predicting the past with Ithaca — Google DeepMind",
   "card_snippet": "Core: Ithaca deep neural network (restores ancient Greek inscriptions, identifies geographic origins, establishes creation dates). Architecture: Sparse multi-he...",
   "card_note": "the snippet ('Core: … Architecture: Sparse multi-he...') is a summary of the page, not its words",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, revelationfirst, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)"
  },
  {
   "id": "B2",
   "origin": "card",
   "role": "Overview card 2",
   "card": "GitHub — GitHub - google-deepmind/ithaca: Restoring and attributing ancient texts using deep neural networks · GitHub",
   "url": "https://github.com/google-deepmind/ithaca",
   "kind": "Overview card (the code repository)",
   "files": {
    "B2-403.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B2-403.html",
     "sha256": "b3e03aaaff81d68730d67392a135ac3fdfdf022880c66632ab188d2fe084cda3"
    }
   },
   "fetched": {
    "at": "2026-10-31T04:39:12Z",
    "http": 403,
    "url": "https://github.com/google-deepmind/ithaca",
    "raw": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B2-403.html",
    "raw_sha256": "b3e03aaaff81d68730d67392a135ac3fdfdf022880c66632ab188d2fe084cda3",
    "agent": "alexanarch-seating/1.0 (https://www.alexanarch.org)",
    "text": [],
    "result": "unreachable: HTTP 403 from the build environment's GitHub gate (a JSON message that GitHub access to the repository is not enabled for this session), saved as B2-403.html; not retried with another agent string or by any other route (no gh, no API, no connector)"
   },
   "names_operator": "yes, in the snippet ('developed by DeepMind and academic partners')",
   "note": "unreachable (BRIEF-NEW-ROW-FIELD §4): the URL from the card's title (repository google-deepmind/ithaca; the paper's Code availability gives github.com/deepmind/ithaca, the organisation's earlier name). The card's snippet stands as its only text (F4).",
   "surfaced_by_T": [
    {
     "slug": "ithaca-deepmind-aio-20261031",
     "card": 2
    }
   ],
   "card_title": "GitHub - google-deepmind/ithaca: Restoring and attributing ancient texts using deep neural networks · GitHub",
   "card_snippet": "Overview: Ithaca is a deep neural network developed by DeepMind and academic partners for restoring, geographically attributing, and chronologically dating anci...",
   "card_note": null,
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, revelationfirst, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)"
  },
  {
   "id": "B3",
   "origin": "card",
   "role": "Overview card 3 (also the census's own page ithaca--home, which redirects here)",
   "card": "Predicting the Past — Predicting the Past",
   "url": "https://predictingthepast.com/",
   "kind": "Overview card (the public interface for Ithaca and Aeneas; ithaca.deepmind.com redirects to it)",
   "files": {
    "B3.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B3.html",
     "sha256": "08a11cf87711fe870d18b83f8adc34ec85b625bad14b3b646dab7052d6d393a9"
    },
    "B3.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B3.txt",
     "sha256": "06b41db902580a817c82f9842d473ba88f1965be698259d506dedf7a0c88e8f7"
    },
    "B3-meta.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B3-meta.txt",
     "sha256": "3f6895b72c46ded7bddb1adf2ba58a8838c1dce03f889e34d7f5e8d357529c74"
    }
   },
   "fetched": {
    "at": "2026-10-31T04:39:13Z",
    "http": 200,
    "url": "https://predictingthepast.com/",
    "raw": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B3.html",
    "raw_sha256": "08a11cf87711fe870d18b83f8adc34ec85b625bad14b3b646dab7052d6d393a9",
    "agent": "alexanarch-seating/1.0 (https://www.alexanarch.org)",
    "census_copy": "ithaca--home (url https://ithaca.deepmind.com/, final https://predictingthepast.com/)",
    "census_sha256": "08a11cf87711fe870d18b83f8adc34ec85b625bad14b3b646dab7052d6d393a9",
    "agrees": true,
    "text": [
     "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B3.txt",
     "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B3-meta.txt"
    ]
   },
   "names_operator": "no (the page's words name Ithaca and Aeneas, not their maker)",
   "note": "The page is a JavaScript application: its extracted text (B3.txt, identical to the census's ithaca--home.txt) is the title and a browser notice; its words are the <title> and the description in its meta tags, extracted verbatim to B3-meta.txt (the card's snippet is title tail plus the description). Byte-identical to the census's saved copy of its own page ithaca--home, which the census lists among unread_sources (http 200; a JavaScript application); carried once, as a card. The application's script bundle was not fetched.",
   "surfaced_by_T": [
    {
     "slug": "ithaca-deepmind-aio-20261031",
     "card": 3
    }
   ],
   "card_title": "Predicting the Past",
   "card_snippet": "Contextualising, restoring, and attributing ancient texts Ithaca and Aeneas are generative AI models designed to assist historians in the study of ancient Greek...",
   "card_note": null,
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, revelationfirst, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)"
  },
  {
   "id": "B4",
   "origin": "card",
   "role": "Overview card 4 (also the census's one source, ithaca--nature)",
   "card": "Nature — Restoring and attributing ancient texts using deep neural networks | Nature",
   "url": "https://www.nature.com/articles/s41586-022-04448-z",
   "kind": "Overview card (the paper: Assael, Sommerschield et al., Nature 603, 280–283, 9 March 2022, open access)",
   "files": {
    "B4.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B4.html",
     "sha256": "7bab9e1b3786b154379e2f2cbf42bda24ffe81228e99404bcaea5af143217fcf"
    },
    "B4.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B4.txt",
     "sha256": "d10c3328114f85d4738563c21ee3136e6942603470b89e88ace9c0ee7b0dc9e1"
    },
    "B4-census.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B4-census.html",
     "sha256": "d2b4df37a93252c3d44127e213464c4bed2ab3a26dbffde58a648645bcad7aed"
    }
   },
   "fetched": {
    "at": "2026-10-31T04:39:15Z",
    "http": 200,
    "url": "https://www.nature.com/articles/s41586-022-04448-z",
    "final_url": "https://www.nature.com/articles/s41586-022-04448-z?error=cookies_not_supported&code=bfb45777-eddd-4f7d-9a38-099aec6b358b",
    "raw": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B4.html",
    "raw_sha256": "7bab9e1b3786b154379e2f2cbf42bda24ffe81228e99404bcaea5af143217fcf",
    "agent": "alexanarch-seating/1.0 (https://www.alexanarch.org)",
    "census_copy": "ithaca--nature",
    "census_sha256": "d2b4df37a93252c3d44127e213464c4bed2ab3a26dbffde58a648645bcad7aed",
    "census_fetched": "2026-10-31T02:52:07Z",
    "agrees": "text identical (B4.txt equals the census's ithaca--nature.txt byte for byte); the raw HTML differs only in per-request tokens (ad-call cache-busters, a CSRF value, reference-anchor ids)",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B4.txt"
    ]
   },
   "names_operator": "yes (authors Yannis Assael, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, Jonathan Prag, Nando de Freitas; affiliations DeepMind, Ca' Foscari, Harvard's Center for Hellenic Studies, AUEB, Oxford)",
   "note": "The census's only source for the row (basis 'operator' for its four evidence quotes), surfaced by T as card 4; refetched with the census's agent string and carried once, as a card, with the census's copy saved beside it (B4-census.html, gunzipped from ithaca--nature.html.gz).",
   "surfaced_by_T": [
    {
     "slug": "ithaca-deepmind-aio-20261031",
     "card": 4
    }
   ],
   "card_title": "Restoring and attributing ancient texts using deep neural networks | Nature",
   "card_snippet": "Model: Ithaca, a deep neural network trained on ancient Greek inscriptions (7th century BC to 5th century AD) from the I.PHI dataset (78,608 texts), simultaneou...",
   "card_note": null,
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, revelationfirst, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)"
  },
  {
   "id": "B5",
   "origin": "wikipedia",
   "role": "Wikipedia (no article on the entity; the section that describes it)",
   "card": "Wikipedia — Google DeepMind §Others, 'Archaeology' (no article 'Ithaca (neural network)' or similar; 'Ithaca' a disambiguation page that does not list the model)",
   "url": "https://en.wikipedia.org/wiki/Google_DeepMind",
   "kind": "encyclopedia section (revision 1380412362) and the disambiguation page of the bare name",
   "files": {
    "B5.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B5.html",
     "sha256": "4e7bfba0afa24409ab25ac687952cd2e16958d318b815e30cb45f07c1ec9a73f"
    },
    "B5.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/B5.txt",
     "sha256": "e0065367800a53a04e8c97d94eb089590cf83a02edd5f447159ed6358eec11c8"
    },
    "W7.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W7.txt",
     "sha256": "91ad081371380a52d1901b8e915b362e19b404b5bdfa6ce4f20ff8bdd12f21a5"
    },
    "W1.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W1.raw",
     "sha256": "11567ee84c27a1249fbe65bff7a643b8ed0fe1bdc419109d83e7108c4cb6d227"
    },
    "W2.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W2.raw",
     "sha256": "3732a8dc9c5a26a4246ec2e6f9107107e740d0f6f5a8b3645e0fd055ed277e8e"
    },
    "W3.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W3.raw",
     "sha256": "edc480d3fce87911b4cee00b27527b08635403b219c839d18954bf8bac3d5794"
    },
    "W4.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W4.raw",
     "sha256": "30fe3bb6eccd18d5339bb8caf4619e358f975e1cbaf76c2b3ad4409379006ebe"
    },
    "W5.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W5.raw",
     "sha256": "20d07d161c01ef8385194b7a85730c73bd55d88bfefffcc031d166d1f5fc933f"
    },
    "W6.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W6.raw",
     "sha256": "1ebc4422bd2be84f26549f2468098fcc7ad986cb4ccfe27465b2ad65e6756226"
    },
    "W7.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W7.raw",
     "sha256": "97c959ea17bdaaa7f42833a0b80e1f5e447970b67cdfa1ac2a26a86efb11bb40"
    },
    "W8.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W8.raw",
     "sha256": "d700293d0bd047ac312802ead90ab6a01168e8f6c1bcd63daa5d69e3855cd598"
    },
    "W9.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W9.raw",
     "sha256": "133497d3b16d94d33e372b86f442d6cc9cf5299a15e996b1182603807f9abb0a"
    },
    "W10.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W10.raw",
     "sha256": "0b776434b568a88e521e24556f10e9053f2de834e735c8cf9e8b6cc0ab8dfdfc"
    },
    "W11.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W11.raw",
     "sha256": "f2610489fa927fd4f4c017d5067267fae04be4a13f4a556cd7bf869d85c6cb22"
    },
    "W12.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W12.raw",
     "sha256": "c4c643ec29a42b7f9f1b0ee31f653f2f147ff1c9ed6fe671b26ca4579e54acb5"
    },
    "W13.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W13.raw",
     "sha256": "4e7bfba0afa24409ab25ac687952cd2e16958d318b815e30cb45f07c1ec9a73f"
    },
    "W14.raw": {
     "path": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W14.raw",
     "sha256": "bf0bb48961467553aae2008971ff67fb64cc32aae0efc6a95b6fe71e57f2eae3"
    }
   },
   "fetched": {
    "responses": [
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W1.raw",
      "url": "https://en.wikipedia.org/w/index.php?title=Ithaca_(neural_network)&action=raw",
      "http": 404,
      "at": "2026-10-31T04:41:04Z",
      "sha256": "11567ee84c27a1249fbe65bff7a643b8ed0fe1bdc419109d83e7108c4cb6d227"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W2.raw",
      "url": "https://en.wikipedia.org/w/index.php?title=Ithaca_(DeepMind)&action=raw",
      "http": 404,
      "at": "2026-10-31T04:41:04Z",
      "sha256": "3732a8dc9c5a26a4246ec2e6f9107107e740d0f6f5a8b3645e0fd055ed277e8e"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W3.raw",
      "url": "https://en.wikipedia.org/w/index.php?title=Ithaca_(artificial_intelligence)&action=raw",
      "http": 429,
      "at": "2026-10-31T04:41:04Z",
      "sha256": "edc480d3fce87911b4cee00b27527b08635403b219c839d18954bf8bac3d5794"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W4.raw",
      "url": "https://en.wikipedia.org/w/index.php?title=Ithaca_(machine_learning)&action=raw",
      "http": 404,
      "at": "2026-10-31T04:41:05Z",
      "sha256": "30fe3bb6eccd18d5339bb8caf4619e358f975e1cbaf76c2b3ad4409379006ebe"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W5.raw",
      "url": "https://en.wikipedia.org/w/index.php?title=Ithaca_(model)&action=raw",
      "http": 404,
      "at": "2026-10-31T04:41:05Z",
      "sha256": "20d07d161c01ef8385194b7a85730c73bd55d88bfefffcc031d166d1f5fc933f"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W6.raw",
      "url": "https://en.wikipedia.org/w/index.php?title=Ithaca&action=raw",
      "http": 200,
      "at": "2026-10-31T04:41:05Z",
      "sha256": "1ebc4422bd2be84f26549f2468098fcc7ad986cb4ccfe27465b2ad65e6756226"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W7.raw",
      "url": "https://en.wikipedia.org/wiki/Ithaca",
      "http": 200,
      "at": "2026-10-31T04:41:05Z",
      "sha256": "97c959ea17bdaaa7f42833a0b80e1f5e447970b67cdfa1ac2a26a86efb11bb40"
     },
     {
      "file": "datasets/negative-of-the-negative/v2/intake/ithaca-20261031/W8.raw",
      "url": "https://en.wikipedia.org/wiki/Ithaca_(neural_network)",
      "http": 404,
      "at": "2026-10-31T04:41:06Z",
      "sha256": "d700293d0bd047ac312802ead90ab6a01168e8f6c1bcd63daa5d69e3855cd598"
     },
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   "quote": "the student annotators and baseline evaluators of the Athens University of Economics and Business (MSc in Digital Methods for the Humanities) and the University of Oxford (Faculty of Classics)",
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   "quote": "we partnered with Google Cloud and Google Arts & Culture to launch a free interactive version of Ithaca . And to aid further research, we have also open sourced our code, the pretrained model, and an interactive Colaboratory notebook.",
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   "quote": "The Ithaca web interface was developed by Justin Grayston, Benjamin Maynard, and Ricardo Cardenas from Google Cloud.",
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   "quote": "we are currently working on versions of Ithaca trained on other ancient languages and historians can already use their datasets in the current architecture to study other ancient writing systems, from Akkadian to Demotic and Hebrew to Mayan",
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   "quote": "Ithaca most commonly refers to: Homer's Ithaca , an island featured in Homer's Odyssey",
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 },
 "field_sources": {
  "B1": "Google DeepMind — Predicting the past with Ithaca — Google DeepMind",
  "B2": "GitHub — GitHub - google-deepmind/ithaca: Restoring and attributing ancient texts using deep neural networks · GitHub",
  "B3": "Predicting the Past — Predicting the Past",
  "B4": "Nature — Restoring and attributing ancient texts using deep neural networks | Nature",
  "B5": "Wikipedia — Google DeepMind §Others, 'Archaeology' (no article 'Ithaca (neural network)' or similar; 'Ithaca' a disambiguation page that does not list the model)"
 }
}