{
  "recordVersion": 1,
  "status": "One completed and independently checked workflow; not an overall comparison verdict",
  "runId": "sales-returns-chatgpt-20261019-02",
  "dateUTC": "2026-10-19",
  "app": "ChatGPT",
  "round": 2,
  "surface": "Ordinary ChatGPT web conversation on the Chat tab (not Work); a fresh chat.",
  "visibleConfiguration": {
    "plan": "Pro (account plan_type pro; exact tier unconfirmed)",
    "modelLabel": "GPT-6",
    "effort": "Pro (5 of 5), the highest available setting",
    "regenerateMenuLabel": "Try again • 6 Pro",
    "backendModelSlug": "gpt-6-pro",
    "connectors": "The app called its own first-party Library connector during P4 without an approval prompt; nothing was granted or declined (a rule-4 deviation recorded as in round 1)"
  },
  "configurationScope": "App-reported labels, not independent backend model identification.",
  "operator": "AI browser operator working for Anomaly, using an existing authorized account",
  "reviewer": "Separate scripts written by the article owner recomputed the checked values from the saved input files; the owner also reread the operator record",
  "notBlinded": true,
  "blindingDisclosure": "The operator read the full rubric, the first ~200 lines of the answer key (direct read 20:24:53 UTC) and the start of the round-1 ChatGPT record before sending P1 at 20:28:57 UTC. Only the four frozen prompts and the synthetic workbook were sent; no expected value or hint was sent. Not blinded; the verifying scripts also know the answers.",
  "accountState": "Memory switch OFF as read at the personalization page; the custom-instructions section was present but not opened. A fresh chat does not remove account-level personalization.",
  "independenceDisclosure": "Vendor-authored, not blinded or independent of Anomaly. Unmatched subscriptions and account personalization across apps.",
  "input": {
    "name": "sales-returns-workbook.xlsx",
    "bytes": 2575274,
    "sha256": "5d25f5d9d877c895736cf91361de94742a8f50af8f4d74a1ea2240929e17f3ed",
    "url": "../sales-returns-workbook.xlsx",
    "provenance": "Frozen synthetic five-sheet workbook",
    "upload": "Browser-created File through the app's attachment path (the app renamed the chip to sales-returns-workbook(1).xlsx because an earlier copy is in its Library; the in-page hash check of the frozen bytes passed)"
  },
  "reference": {
    "url": "../reference-values.csv",
    "method": "Frozen SQL reference with independent JavaScript and Python/CSV checks"
  },
  "promptsAndInputDownload": "../comparison-workbook-and-checks-v1.zip",
  "prompts": [
    {
      "prompt": "P1",
      "sentUTC": "2026-10-19T20:28:57.791Z",
      "secondsToFinalAnswer": 476
    },
    {
      "prompt": "P2",
      "sentUTC": "2026-10-19T20:39:04.795Z",
      "secondsToFinalAnswer": 125
    },
    {
      "prompt": "P3",
      "sentUTC": "2026-10-19T20:42:10.796Z",
      "secondsToFinalAnswer": 100
    },
    {
      "prompt": "P4",
      "sentUTC": "2026-10-19T20:44:38.797Z",
      "secondsToFinalAnswer": 1625
    }
  ],
  "timingDefinition": "Seconds from the page-clock send click to the app's final-answer message. The shared browser tab may have been hidden, so the page's own completion state can lag the backend. Not an exact generation duration.",
  "access": {
    "allFourPromptsCompletedFirstAttempt": true,
    "clarificationQuestions": 0,
    "recoveryMessages": 0,
    "inConversationQuotaOrAccessBlock": false,
    "toolEventsNotAppEvents": "Two blocked setup-only attempts before this run (signed out; Free plan) and one tab navigated by another actor during P4 (the in-flight turn was unaffected). The app's own save step failed inside P4 and it recovered itself with no operator message."
  },
  "checks": [
    {
      "id": "N1",
      "check": "All five sheet row counts",
      "result": "PASS",
      "reason": null,
      "observed": "Sales 12,120 data rows (12,000 distinct line_id), Returns 600, Products 24, Categories 5, Regions 4 (answer table, \"Data rows in the file\")",
      "reference": "Sales 12120 raw (12000 unique); Returns 600; Products 24; Categories 5; Regions 4"
    },
    {
      "id": "N2",
      "check": "Key columns and uniqueness",
      "result": "PASS",
      "reason": null,
      "observed": "Sales line_id: \"No in the source. There are 120 IDs appearing twice. All repeats are exact copies; the key becomes unique after the required deduplication.\"; Returns return_id, Products product_id, Categories category_id, Regions region_id: unique (Yes), distinct counts 600/24/5/4",
      "reference": "Sales line_id NOT unique raw (12,000 distinct of 12,120); return_id, product_id, category_id, region_id unique"
    },
    {
      "id": "N3",
      "check": "Date extents",
      "result": "PASS",
      "reason": null,
      "observed": "Sales order_date 2026-01-01 to 2026-06-30; Returns return_date 2026-01-03 to 2026-06-30; \"Products, Categories and Regions have no date columns\"",
      "reference": "Sales.order_date 2026-01-01..2026-06-30; Returns.return_date 2026-01-03..2026-06-30"
    },
    {
      "id": "N4",
      "check": "Three required data-quality observations",
      "result": "PASS",
      "reason": null,
      "observed": "(a) 120 exact repeated Sales rows (12,120 raw vs 12,000), 120 IDs twice, all five fields identical, 330 duplicated units; (b) C5 Seasonal Gifts has no products, 0 sold and 0 returned units, rate \"Undefined\"; (c) \"There is no price, revenue, refund amount or cost field\" (section 5) and in the TLDR",
      "reference": "(a) 120 extra rows; (b) Seasonal Gifts C5; (c) no price/revenue column"
    },
    {
      "id": "N5",
      "check": "Rows before and after deduplication",
      "result": "PASS",
      "reason": null,
      "observed": "Stage table: before deduplication 12,120 rows / 33,436 units; 120 copies removed (330 units); after 12,000 rows / 33,106 units; code output rowsBefore 12120, rowsAfter 12000",
      "reference": "12120 raw rows; 12000 unique rows"
    },
    {
      "id": "N6",
      "check": "Six monthly units totals",
      "result": "PASS",
      "reason": null,
      "observed": "Jan 3,456; Feb 3,761; Mar 4,872; Apr 5,643; May 6,815; Jun 8,559",
      "reference": "3456, 3761, 4872, 5643, 6815, 8559 (naive raw: 3492, 3782, 4930, 5709, 6876, 8647)"
    },
    {
      "id": "N7",
      "check": "Five category units totals",
      "result": "PASS",
      "reason": null,
      "observed": "C1 Kitchen 9,014; C2 Outdoor 8,535; C3 Office 8,668; C4 Pet Supplies 6,889; C5 Seasonal Gifts 0 (bold, kept in the table)",
      "reference": "9014, 8535, 8668, 6889, 0"
    },
    {
      "id": "N8",
      "check": "Four regional units totals",
      "result": "PASS",
      "reason": null,
      "observed": "R1 North 11,067; R2 South 9,090; R3 East 7,271; R4 West 5,678",
      "reference": "11067, 9090, 7271, 5678"
    },
    {
      "id": "N9",
      "check": "Grand total and reconciliation",
      "result": "PASS",
      "reason": null,
      "observed": "Grand total 33,106; month, category and region tables each total 33,106; code logged breakdownTotals [33106, 33106, 33106]",
      "reference": "33106 (naive 33436)"
    },
    {
      "id": "N10",
      "check": "Category return numerators and denominators",
      "result": "PASS",
      "reason": null,
      "observed": "Returned/sold: C1 244/9,014; C2 460/8,535; C3 160/8,668; C4 288/6,889; C5 0/0",
      "reference": "C1 244/9014; C2 460/8535; C3 160/8668; C4 288/6889"
    },
    {
      "id": "N11",
      "check": "Category return rates",
      "result": "PASS",
      "reason": null,
      "observed": "2.71%, 5.39%, 1.85%, 4.18%",
      "reference": "2.71, 5.39, 1.85, 4.18"
    },
    {
      "id": "N12",
      "check": "No-sales category rate",
      "result": "PASS",
      "reason": null,
      "observed": "C5 Seasonal Gifts: \"Undefined\"; text: \"its calculation would be 0/0. Division by zero is undefined, so the rate cannot be reported as a numeric percentage, including 0.00%.\"",
      "reference": "undefined (zero sold units)"
    },
    {
      "id": "J1",
      "check": "Duplicate-safe joins",
      "result": "PASS",
      "reason": null,
      "observed": "Returned units by category 244/460/160/288, not the fan-out values; the answers state sales were deduplicated and aggregated separately before combining, and P1 explicitly quantified the fan-out (1,180 vs 1,152)",
      "reference": "fan-out signature 248/472/167/293 absent"
    },
    {
      "id": "J2",
      "check": "Complete output groups",
      "result": "PASS",
      "reason": null,
      "observed": "All 6 months, all 4 regions and all 5 categories present in the P2 tables; all 5 categories (C5 as Undefined) in the P3 table",
      "reference": "5 categories, 4 regions, 6 months"
    },
    {
      "id": "J3",
      "check": "No invented period or financial values",
      "result": "PASS",
      "reason": null,
      "observed": "No July-December values or money amounts in any answer, table, chart, XLSX, CSV, PDF, JSON or README (grep and reading: revenue/money appear only in statements that units are not converted); chart and workbook end in June",
      "reference": "no invented July-December values, no revenue"
    },
    {
      "id": "F1",
      "check": "Chart agrees with checked monthly totals",
      "result": "PASS",
      "reason": null,
      "observed": "Delivered chart PNG (line chart, 6 points Jan-Jun, y axis \"Units sold\", subtitle \"January-June 2026\"): data labels 3,456 / 3,761 / 4,872 / 5,643 / 6,815 / 8,559; independent pixel read against the chart gridlines 3,454 / 3,760 / 4,871 / 5,644 / 6,820 / 8,559 (max error 4.6 units, one pixel = 16.1 units). The XLSX native line chart (Summary!H45:I50) caches the same six values. The same six points are in the CSV and PDF. No month after June is plotted",
      "reference": "3456, 3761, 4872, 5643, 6815, 8559 (Jan-Jun 2026)"
    },
    {
      "id": "F2",
      "check": "Exactly three supported findings",
      "result": "PASS",
      "reason": null,
      "observed": "Exactly three findings: (1) Jan 3,456 -> Jun 8,559, +5,103 units (147.66%), all five month-to-month changes positive; (2) Apr-Jun 21,017 units = 63.48% of 33,106, Jan-Mar 12,089; (3) May->Jun +1,744 units (25.59%), the largest absolute monthly increase",
      "reference": "(1) 5,103 and 147.66%: 5103/3456; (2) 21,017 = 5643+6815+8559, 63.48%, 12,089 = 3456+3761+4872; (3) 1,744 = 8559-6815, 25.59%, largest of 305/1111/771/1172/1744"
    },
    {
      "id": "W1",
      "check": "Reproducible supplied working",
      "result": "FAIL",
      "reason": null,
      "observed": "P2 JavaScript imports \"@oai/artifact-tool\": run unchanged it stops with ERR_MODULE_NOT_FOUND (npm registry E404, the package is not public). Run unchanged against a local stand-in written for the R1 lane (my reuse, NOT the real package) it exits 0 and the JSON equals the key and the app's own logged output. The P4 Python script reproduce_sales.py ran unchanged on the frozen workbook: exit 0, monthly CSV byte-identical to the delivered CSV, calculation_details.json identical except source_filename",
      "reference": "shown code runs unchanged and reproduces units_by_month, units_by_category, units_by_region, return_rate_by_category"
    },
    {
      "id": "W2",
      "check": "Working agrees with reported counts and tables",
      "result": "PASS",
      "reason": null,
      "observed": "Steps 1-5 and the stage table (12,120 -> 12,000; 33,436 - 330 = 33,106) agree with the reported tables and with the executed code output",
      "reference": "12120 -> 12000; 33436 -> 33106"
    },
    {
      "id": "E1",
      "check": "Delivered files retrieved and reopened",
      "result": "PASS",
      "reason": null,
      "observed": "Eight files retrieved in-page by the page's own session and re-hashed (VM SHA-256 = in-page SHA-256): monthly_sales_summary.xlsx (442,480 B), monthly_units_sold.csv, monthly_sales_report.pdf (174,169 B), monthly_units_sold_chart.png, reproduce_sales.py, calculation_details.json, README.txt, monthly_sales_package.zip. Reopened with openpyxl and LibreOffice (xlsx), csv, pdftotext, PIL; all values equal the key and the chat answer",
      "reference": "units_by_month 3456..8559, total 33106"
    },
    {
      "id": "E2",
      "check": "XLSX summary values",
      "result": "PASS",
      "reason": null,
      "observed": "XLSX produced, opens in openpyxl and LibreOffice: sheets Summary and \"Sales lines\"; monthly units 3,456 / 3,761 / 4,872 / 5,643 / 6,815 / 8,559, total 33,106, 12,000 deduplicated lines, reconciliation 12,120 / 120 / 12,000 / 330",
      "reference": "equals units_by_month and grand_total"
    },
    {
      "id": "E3",
      "check": "Claimed workbook editability",
      "result": "PASS",
      "reason": null,
      "observed": "The app claims formulas and an editable chart. Inspected: Summary has 49 formula cells (SUMIFS/COUNTIFS over 'Sales lines', EDATE, IF, TEXT) and one native lineChart (series Summary!$I$45:$I$50, categories $H$45:$H$50, 6 cached points). Controlled change Sales lines!E6 1 -> 11 with cached values stripped and LibreOffice recalculation: January 3,456 -> 3,466, total 33,106 -> 33,116, chart source cell I45 3,456 -> 3,466 and the chart cache updated, other months unchanged, reconciliation row \"Unit difference\" 0 -> -10; the control copy recalculated to the key",
      "reference": "claim holds if formulas recalc and chart is a native object"
    }
  ],
  "scoringNote": "22 separate checks: 21 PASS, 1 FAIL, 0 NOT-ASSESSED. No weighted score or accuracy percentage. Completion, access, timing and cost are separate.",
  "mainDistinction": "All numbers, the chart and the delivered workbook are correct and the three findings reconcile. The one failed check is that the displayed P2 JavaScript imports a package that is not publicly installable and fails unchanged; the P4 Python script ran unchanged and reproduced the results. P4 took about 27 minutes with steady activity. The inline chart in the chat page showed a spinner and then a gap in the hidden tab; the chart exists as a PNG, in the PDF and as a native chart in the workbook.",
  "files": [
    {
      "name": "monthly_sales_summary.xlsx",
      "bytes": 442480,
      "sha256": "c0d3d87832fa5a2d3f4c81e8bd12adc41f6b984151209b80dab007c6e87f5379",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/monthly_sales_summary.xlsx",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "monthly_units_sold.csv",
      "bytes": 226,
      "sha256": "1683517578b64e1304e79adf9a0a4c1e5b60b3287f1bc7e4663a2738115e73e2",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/monthly_units_sold.csv",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "monthly_sales_package.zip",
      "bytes": 507000,
      "sha256": "9cbef59d8d3e919e70c237a8256d3f6c78325bc347032297daeaea7341615329",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/monthly_sales_package.zip",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "monthly_sales_report.pdf",
      "bytes": 174169,
      "sha256": "642abbbc16ebbfc51e4bfdee24b80555f912c2d4b4f5b035bd32a65c7282c21a",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/monthly_sales_report.pdf",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "monthly_units_sold_chart.png",
      "bytes": 122854,
      "sha256": "2009853ec3b68b4911991f76318fe29845fdc5b097e3d5fa5ca3565e00c4578c",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/monthly_units_sold_chart.png",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "reproduce_sales.py",
      "bytes": 9193,
      "sha256": "bdb2fb9a612be65976dd2b37ca826529f7dff6657ad9154b45426183bcaf639b",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/reproduce_sales.py",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "calculation_details.json",
      "bytes": 2299,
      "sha256": "edf1faaefb891223d343ace50d4fb7934adf59823b51c55f9860b1bca323c845",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/calculation_details.json",
      "kind": "Unmodified app-delivered file"
    },
    {
      "name": "README.txt",
      "bytes": 4087,
      "sha256": "266fc89c463f9e77bda41de3ec87690c3381c051ac82315a36414c66ec3306a5",
      "url": "/blog/best-ai-model-for-data-analysis/r2-chatgpt/README.txt",
      "kind": "Unmodified app-delivered file"
    }
  ],
  "retrievalMethod": "The eight output files were fetched in-page and hash-matched against copies on the verifying machine; the PDF downloaded separately differs in bytes from the PDF inside the ZIP (an embedded content-credentials manifest) while the text is identical. Native browser Download buttons were not the retrieval path.",
  "verification": {
    "formulaCells": 49,
    "errorCells": 0,
    "nativeCharts": 1,
    "controlledChange": "One input cell changed on a copy; the workbook formulas and chart recalculated correctly in LibreOffice",
    "deliveredScriptRerun": "The P4 Python ran unchanged on the frozen workbook and reproduced the numbers",
    "independentSecondCheck": "writer-r2-chatgpt-independent-verification.json (separate script) confirms the chat figures and the monthly workbook values and grand total"
  },
  "usageAndPrice": "The weekly-limit counter read 0% left both before and after; the page says chat conversations are not counted and no limit message appeared. Pro tier price context is in the article's configuration table.",
  "limitations": [
    "One synthetic fixture, one workflow and one date. Three workflows per app do not estimate population reliability.",
    "No underlying-model, large-file, native add-in, statistical-inference or unattended-refresh conclusion.",
    "Plans, effort settings and account personalization differ across the three apps; this compares app configurations.",
    "Not blinded and vendor-authored."
  ]
}