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Excel.ChartSeriesCollection.add::Backward_EnronFuse.1009-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1009-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Utilities sheet, create a clustered column chart comparing the monthly customer charges for Small Commercial and Medium Commercial. Title it “Monthly Customer Charge Comparison,” name the chart “CommercialRateComparison,” hide the legend, and place it from G4 to N18. | The Small Commercial and Medium Commercial labels are in nonadjacent cells B4 and E4, while their monthly customer charges are in B6 and E6. Creating a chart from these separated cells requires adding a series and explicitly assigning its categories and values. | Utilities contains a clustered column chart named "CommercialRateComparison" positioned at G4:N18, titled "Monthly Customer Charge Comparison", with no legend and one series named "Monthly Customer Charge" whose categories are ["Small Commercial", "Medium Commercial"] and values are [7.5, 110]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const charts = sheet.charts;
const oldChart = charts.getItemOrNullObject("CommercialRateComparison");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart.delete()... | null | outputs/train/84128436c7ec4d4b1b1fa8aa1f4d04c250826466cb23142f9956c50bf2eee874/gold.xlsx | Utilities contains a clustered column chart named "CommercialRateComparison" positioned at G4:N18, titled "Monthly Customer Charge Comparison", with no legend and one series named "Monthly Customer Charge" whose categories are ["Small Commercial", "Medium Commercial"] and values are [7.5, 110].
| 2026-09-08T09:01:28.791684+00:00 | Chart | 672 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n const charts = sheet.charts;\n\n // Replace any existing chart with the requested name.\n const existing = charts.getItemOrNullObject(\"CommercialRateComparison\");\n existing.load(\"isNull... | 84128436c7ec4d4b1b1fa8aa1f4d04c250826466cb23142f9956c50bf2eee874 | workbooks/Backward_EnronFuse.1009-File.xlsx | 606e05d965ed602272c59e288dd16cceb459220812ef14788a0f47a71d79a5b1 | f1b7c503714b784989f10ca3813f3a2bbcd4e33a86d14bccaf1d8db324390adb | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.100-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.100-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a line chart on Sheet1 comparing Debt service/fixed pmt and O&M across periods 1 through 9. Name the chart FixedCostsByPeriod, title it “Fixed Costs by Period,” and place it from O3 to W18. | The periods 1–9 and the two complete fixed-cost rows are split across three nonadjacent blocks on Sheet1. A new line chart can combine those blocks into continuous nine-point series by explicitly adding each series and assigning its category and value arrays. | Sheet1 contains a new line chart named "FixedCostsByPeriod" positioned from O3 to W18, titled "Fixed Costs by Period", with categories [1,2,3,4,5,6,7,8,9] and two series: "Debt service/fixed pmt" = [40,40,40,40,40,40,40,40,40] and "O&M" = [9,9,9,9,9,9,9,9,9]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const periodRanges = [
sheet.getRange("C5:E5"),
sheet.getRange("G5:I5"),
sheet.getRange("K5:M5")
];
const debtRanges = [
sheet.getRange("C9:E9"),
sheet.getRange("G9:I9"),
sheet.getRange("K9... | null | outputs/train/ad7cdd5b51d9981eecc73c45304de23fab9f9ab5fcb0bd7dea3657bb35a5127b/gold.xlsx | Sheet1 contains a new line chart named "FixedCostsByPeriod" positioned from O3 to W18, titled "Fixed Costs by Period", with categories [1,2,3,4,5,6,7,8,9] and two series: "Debt service/fixed pmt" = [40,40,40,40,40,40,40,40,40] and "O&M" = [9,9,9,9,9,9,9,9,9].
| 2026-09-08T09:02:42.717592+00:00 | Chart | 162 | 2 | 5 | 2 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build a compact, contiguous source range for the nine periods.\n const source = sheet.getRange(\"X3:Z12\");\n source.values = [\n [\"Period\", \"Debt service/fixed pmt\", \"O&M\"],\n ... | ad7cdd5b51d9981eecc73c45304de23fab9f9ab5fcb0bd7dea3657bb35a5127b | workbooks/Backward_EnronFuse.100-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | 771245ec26d14e84c47d607b7f6b71ff726b78f6fe1c8e912ca177a9770364ca | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1010-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1010-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, create a horizontal clustered bar chart that directly compares the Monthly Customer Charge for Small Commercial (B4/B6) and Medium Commercial (E4/E6), without adding a helper table. Title it “Monthly Customer Charge Comparison,” show value labels, hide the legend, set the value axis from 0 to 120 in incre... | The two commercial classes and their monthly customer charges are stored in nonadjacent cells (labels B4/E4 and values B6/E6). Creating one clean series directly from those dispersed cells, without a helper table, requires adding a chart and explicitly defining its category and value references. | Utilities contains a new clustered bar chart named "MonthlyCustomerChargeChart" at G4:N18, titled "Monthly Customer Charge Comparison", with one series whose categories are Small Commercial and Medium Commercial and whose values are 7.5 and 110, respectively; value labels are shown, the legend is hidden, and the value ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
// Add the chart from the row containing the two dispersed numeric values.
const chart = sheet.charts.add(
Excel.ChartType.barClustered,
sheet.getRange("B6:E6"),
Excel.ChartSeriesBy.rows
);
chart... | null | outputs/train/1662f124951a51627e9160477c4252bf37c9782f8c0473300c98647b84f80ab9/gold.xlsx | Utilities contains a new clustered bar chart named "MonthlyCustomerChargeChart" at G4:N18, titled "Monthly Customer Charge Comparison", with one series whose categories are Small Commercial and Medium Commercial and whose values are 7.5 and 110, respectively; value labels are shown, the legend is hidden, and the value ... | 2026-09-08T09:03:56.386485+00:00 | Chart | 772 | 2 | 5 | 2 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n const charts = sheet.charts;\n\n const oldChart = charts.getItemOrNullObject(\"MonthlyCustomerChargeComparison\");\n oldChart.load(\"isNullObject\");\n await context.sync();\n if (!oldChar... | 1662f124951a51627e9160477c4252bf37c9782f8c0473300c98647b84f80ab9 | workbooks/Backward_EnronFuse.1010-File.xlsx | 606e05d965ed602272c59e288dd16cceb459220812ef14788a0f47a71d79a5b1 | 846b5d1203ba0ffda2a0f04d745d91a89fe0d4d3cf034d44f94f94e559663e02 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1011-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1011-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Using the Small Commercial and Medium Commercial charges on the Utilities sheet, compare their estimated monthly bills assuming demand never exceeds 25 kW. Build a table for 0, 1,000, 2,000, 3,000, 4,000, and 5,000 kWh plus the exact break-even usage, then create an XY scatter chart titled “Commercial Bill Break-Even” ... | The Utilities sheet provides the fixed monthly charges in B6/E6 and energy rates in B11/E11. Calculating the exact intersection, inserting it among usage scenarios, charting both resulting bill series, and adding a linear trendline to each makes the task difficult while remaining grounded in the workbook. The code uses... | Utilities!G4:I11 contains [["Monthly kWh","Small Commercial Bill","Medium Commercial Bill"],[0,7.5,110],[1000,51.5,133.2],[2000,95.5,156.4],[3000,139.5,179.6],[4000,183.5,202.8],[4927.884615384615,224.32692307692307,224.32692307692307],[5000,227.5,226]]; Utilities!K4:K5 contains [["Break-even kWh"],[4927.884615384615]]... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const smallMonthlyCell = sheet.getRange("B6");
const mediumMonthlyCell = sheet.getRange("E6");
const smallRateCell = sheet.getRange("B11");
const mediumRateCell = sheet.getRange("E11");
smallMonthlyCell.lo... | null | outputs/train/840d31972dba6deed57c42699739928fbe56680767ca1cb3e77f7fe217737697/gold.xlsx | Utilities!G4:I11 contains [["Monthly kWh","Small Commercial Bill","Medium Commercial Bill"],[0,7.5,110],[1000,51.5,133.2],[2000,95.5,156.4],[3000,139.5,179.6],[4000,183.5,202.8],[4927.884615384615,224.32692307692307,224.32692307692307],[5000,227.5,226]]; Utilities!K4:K5 contains [["Break-even kWh"],[4927.884615384615]]... | 2026-09-08T09:04:53.949073+00:00 | Chart | 826 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n // Remove a prior output chart/table if this task is rerun.\n const oldChart = sheet.charts.getItemOrNul... | 840d31972dba6deed57c42699739928fbe56680767ca1cb3e77f7fe217737697 | workbooks/Backward_EnronFuse.1011-File.xlsx | e11e7e7508c852ed664d77a85c42141c7dcbbddd555640964b25bd97d8c9949a | 18ea9a98ecd32e1c7bc004959dcc48a745adb854a7f7c0bcfd81cc4ed0bd504f | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1004-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1004-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, replace any existing chart named `RevenueExpenseTrend` with a line chart named exactly `RevenueExpenseTrend` at exactly `M3:U18`. Use only the 12 monthly dates in `A4:A15`, Revenue in `H4:H15`, and Expense in `I4:I15`—do not include headers, totals, or blank rows. Configure exactly two series: Revenue as a s... | The monthly dates are in A4:A15, while Revenue and Expense are separated into H4:H15 and I4:I15. Creating the chart from Revenue first and explicitly adding Expense exercises the series collection API; the exact axis, series, naming, replacement, and positioning requirements require substantial configuration beyond a b... | Sheet1 contains exactly one chart named "RevenueExpenseTrend", anchored at M3:U18 and titled "Revenue vs Expense by Month". It has exactly two series using categories A4:A15: Revenue uses H4:H15 and is a solid #4472C4 line; Expense uses I4:I15 and is a dashed #ED7D31 line. The legend is at the bottom. The category axis... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
// Ensure rerunning the task replaces, rather than duplicates, the named chart.
const existingChart = charts.getItemOrNullObject("RevenueExpenseTrend");
existingChart.load("isNullObj... | null | outputs/train/65d5760abe32e8e0036cd7affcfe5f3c16c380535f6901e9ab4bd144e7050fa4/gold.xlsx | Sheet1 contains exactly one chart named "RevenueExpenseTrend", anchored at M3:U18 and titled "Revenue vs Expense by Month". It has exactly two series using categories A4:A15: Revenue uses H4:H15 and is a solid #4472C4 line; Expense uses I4:I15 and is a dashed #ED7D31 line. The legend is at the bottom. The category axis... | 2026-09-08T09:06:03.731074+00:00 | Chart | 417 | 0 | 5 | 2 | 1 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const existing = sheet.charts.getItemOrNullObject(\"RevenueExpenseTrend\");\n existing.load(\"isNullObject\");\n await context.sync();\n if (!existing.isNullObject) {\n existing.delete();\n... | 65d5760abe32e8e0036cd7affcfe5f3c16c380535f6901e9ab4bd144e7050fa4 | workbooks/Backward_EnronFuse.1004-File.xlsx | 07214646d79ba7d7d3bcc66fff50330037178324aabbae295515ff69342b5f6a | 9ccc4b26bccae045f76226bab595c1d2c2b5fe73fe118099d1b118e0ef72e6ab | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1017-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1017-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Utilities sheet, create a clustered column chart comparing the Energy Charge rates for Small Commercial and Medium Commercial. Title it “Commercial Energy Charge Comparison” and place it from G4 to N18. | The two energy-charge rates are stored in nonadjacent cells, Utilities!B11 and Utilities!E11. Creating the chart with a manually added series allows those values to be plotted together under the customer categories from B4 and E4. | Utilities contains a clustered column chart named “CommercialEnergyChargeComparison” positioned at G4:N18, titled “Commercial Energy Charge Comparison”, with one series named “Energy Charge ($/kWh)”: Small Commercial = 0.044 and Medium Commercial = 0.0232. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
// Make the operation repeatable by removing a prior chart with this name.
const existingChart = sheet.charts.getItemOrNullObject("CommercialEnergyChargeComparison");
existingChart.load("isNullObject");
await... | null | outputs/train/fc6d0f3ce8481fdb4b84cea8c96f30fcf146b96961319ef428accd274fd62cde/gold.xlsx | Utilities contains a clustered column chart named “CommercialEnergyChargeComparison” positioned at G4:N18, titled “Commercial Energy Charge Comparison”, with one series named “Energy Charge ($/kWh)”: Small Commercial = 0.044 and Medium Commercial = 0.0232.
| 2026-09-08T09:07:26.104445+00:00 | Chart | 1,131 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n // Remove an existing chart with the requested title/name if this is rerun.\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n for (const existing of... | fc6d0f3ce8481fdb4b84cea8c96f30fcf146b96961319ef428accd274fd62cde | workbooks/Backward_EnronFuse.1017-File.xlsx | 4c1a3874872fa952ef6c79c47402afaec8192782bc5a990ff2f7187b06ea6640 | 8835eaeac00a11625e9183d1e614397455e5810b2b019c6a9baad913a1f75006 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1008-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1008-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, create a visible monthly-bill scenario table in G4:J8 and a clustered column chart over L4:T20. Use these exact headers: G4 “Energy Usage (kWh)”, H4 “Scenario Label”, I4 linked to B4, and J4 linked to E4. Enter 0, 500, 1000, and 2000 in G5:G8. In H5:H8, use formulas linked to A6 and A11 to display “0 kWh ... | The visible table derives scenario labels and monthly bills from Utilities!A6, A11, B6, B11, E6, and E11. The chart series are added with SERIES formulas referencing that table, so they contain no hard-coded chart values. To avoid leaving the chart with zero series, the replacement series are added before the automatic... | Utilities!G4:J8 contains headers [Energy Usage (kWh), Scenario Label, Small Commercial, Medium Commercial]. Its four scenarios display Small Commercial bills [$7.50, $29.50, $51.50, $95.50] and Medium Commercial bills [$110.00, $121.60, $133.20, $156.40]. H5:H8 contains formulas linked to A6 and A11 and displays [0 kWh... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
// Remove only a prior copy of the requested chart, if one exists.
const priorChart = sheet.charts.getItemOrNullObject("CommercialBillScenarios");
priorChart.load("isNullObject");
await context.sync();
if (... | null | outputs/train/8dcaca89329337626a28f39addf73661e07cd354de75188c86f97d8651ba9e9d/gold.xlsx | Utilities!G4:J8 contains headers [Energy Usage (kWh), Scenario Label, Small Commercial, Medium Commercial]. Its four scenarios display Small Commercial bills [$7.50, $29.50, $51.50, $95.50] and Medium Commercial bills [$110.00, $121.60, $133.20, $156.40]. H5:H8 contains formulas linked to A6 and A11 and displays [0 kWh... | 2026-09-08T09:08:08.041681+00:00 | Chart | 621 | 0 | 5 | 3 | 1 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
... | 8dcaca89329337626a28f39addf73661e07cd354de75188c86f97d8651ba9e9d | workbooks/Backward_EnronFuse.1008-File.xlsx | 82754f7aadf6651438cf922fd85b38962c85536dd2daaacc249303a35a443db8 | 965280307ad39be2e2bfa041f00cc904ef1326b2d9a0e7e804bf368a4b1ce33d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1014-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1014-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Utilities sheet, find the section labeled “Example 1)” within the used data and locate its Peak and Total rows. Confirm both corresponding amounts are numeric, then create a clustered column chart linked to those source cells. The chart must be named exactly Example1PeakTotalChart, use the two metric names as ca... | The revised solution uses only plain JavaScript syntax. It searches the Utilities used range for the Example 1 section, validates the numeric Peak and Total values, derives linked category and value formulas, removes only duplicate charts with the target name, and explicitly applies all chart formatting and accessibili... | Utilities contains exactly one chart named Example1PeakTotalChart at G4:N18, with all unrelated charts preserved. It is a clustered column chart titled “Example 1 Peak and Total” with exactly one series named “Example 1”, category formula ='Utilities'!$B$16:$B$17, value formula ='Utilities'!$C$16:$C$17, categories Peak... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const used = sheet.getUsedRange();
used.load(["values", "rowIndex", "columnIndex", "rowCount", "columnCount"]);
await context.sync();
const values = used.values;
const normalize = (value) => String(value ==... | null | outputs/train/657b929d9ef0c7ab281462aa9453152c31472a78e0b843a62a82aa5524c485e5/gold.xlsx | Utilities contains exactly one chart named Example1PeakTotalChart at G4:N18, with all unrelated charts preserved. It is a clustered column chart titled “Example 1 Peak and Total” with exactly one series named “Example 1”, category formula ='Utilities'!$B$16:$B$17, value formula ='Utilities'!$C$16:$C$17, categories Peak... | 2026-09-08T09:08:12.792298+00:00 | Chart | 976 | 0 | 5 | 4 | 2 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
... | 657b929d9ef0c7ab281462aa9453152c31472a78e0b843a62a82aa5524c485e5 | workbooks/Backward_EnronFuse.1014-File.xlsx | 3987ca7fe061a1715f566af4cab3dc093c8c38603954144abd47f784647056dd | cbf34086d73ce70fe0b1d3bbbe87165d717d68c419f0c7b24b2404a82969f839 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1012-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1012-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, build a Medium Commercial monthly-charge sensitivity chart using the tariff information already on the sheet. Find the Medium Commercial demand threshold and per-kW excess charge from its Demand Charge entry, its Energy Charge rate, and the Peak and Total usage in the block labeled “Example 1).” In G21:H2... | The revised solution uses only runtime-valid JavaScript while retaining the harder workbook interpretation: it locates the Medium Commercial tariff and Example 1 inputs by their labels, derives a sensitivity table, creates an idempotent single-series XY chart, and adds exactly one linear trendline. | Utilities!G21:H25 contains [["Peak kW","Projected charge"],[25,165.6016],[27.44,174.3856],[30,183.6016],[35,201.6016]], with G22:G25 formatted "0.00" and H22:H25 formatted "$#,##0.00". Utilities contains exactly one chart named "Example1TrendChart" positioned at G4:N18: an XY scatter chart with one marker-only series n... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const used = sheet.getUsedRange();
used.load(["values", "rowIndex", "columnIndex"]);
await context.sync();
const values = used.values;
const baseRow = used.rowIndex;
const baseColumn = used.columnIndex;
... | null | outputs/train/40a01c097350c53a15c68dc14a279c3b445159d762fbaeca3f0b5102e07b052b/gold.xlsx | Utilities!G21:H25 contains [["Peak kW","Projected charge"],[25,165.6016],[27.44,174.3856],[30,183.6016],[35,201.6016]], with G22:G25 formatted "0.00" and H22:H25 formatted "$#,##0.00". Utilities contains exactly one chart named "Example1TrendChart" positioned at G4:N18: an XY scatter chart with one marker-only series n... | 2026-09-08T09:09:08.829153+00:00 | Chart | 877 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n // Read the tariff and Example 1 inputs already present on the sheet.\n const demandEntry = sheet.getRange(\"E8\");\n const energyRateCell = sheet.getRange(\"E11\");\n const examplePeakCe... | 40a01c097350c53a15c68dc14a279c3b445159d762fbaeca3f0b5102e07b052b | workbooks/Backward_EnronFuse.1012-File.xlsx | f6bd2f656676322767d7734bb328d7e8d90efd107b8c9a966e30e492f3b70a64 | b8051fccaadee074c646dffb61494506be8410a145019b0c985cb0b7b1b4f38d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1018-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1018-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a chart-only comparison on the Utilities sheet without adding or rearranging worksheet data. Use Small Commercial and Medium Commercial as the categories, show Monthly Customer Charge as clustered columns on the primary axis, and show Energy Charge as a line with markers on the secondary axis. Display values on ... | The two tariff classes are stored as nonadjacent headings in B4 and E4, while their fixed and usage charges are in B6/E6 and B11/E11. A custom combo chart requires adding separately constructed series so the much smaller energy rates can use a secondary axis rather than being visually flattened by the monthly charges. | Utilities contains a new combo chart named "CommercialTariffRateProfile" at G4:N20 titled "Commercial Tariff Rate Profile", with categories ["Small Commercial", "Medium Commercial"], primary-axis clustered-column series "Monthly Customer Charge" = [7.5, 110], secondary-axis line-with-markers series "Energy Charge" = [0... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const classHeadings = sheet.getRanges("B4,E4");
const monthlyChargeCells = sheet.getRanges("B6,E6");
const energyChargeCells = sheet.getRanges("B11,E11");
const monthlyLabelCell = sheet.getRange("A6");
cons... | null | outputs/train/965e4ad85620b67e6c8bc4a2146bb1509717872b0ff81cba651d4b1d44516c06/gold.xlsx | Utilities contains a new combo chart named "CommercialTariffRateProfile" at G4:N20 titled "Commercial Tariff Rate Profile", with categories ["Small Commercial", "Medium Commercial"], primary-axis clustered-column series "Monthly Customer Charge" = [7.5, 110], secondary-axis line-with-markers series "Energy Charge" = [0... | 2026-09-08T09:09:52.253015+00:00 | Chart | 1,182 | 2 | 5 | 2 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n // Create the chart from an existing cell only as a host, then replace its series.\n const chart = sheet.charts.add(Excel.ChartType.columnClustered, sheet.getRange(\"B6\"));\n chart.name =... | 965e4ad85620b67e6c8bc4a2146bb1509717872b0ff81cba651d4b1d44516c06 | workbooks/Backward_EnronFuse.1018-File.xlsx | 2dadcb3bba19b5d353ae7a13a51f78270ad68495d5d6d402ebbbd69603388749 | 18ff18d5c8e8c31ce7017d738efbe2cd69c501834b29ed793aad47935298154d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1016-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1016-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a combo chart on Utilities using Small Commercial and Medium Commercial as the two categories from B4 and E4. Plot their Monthly Customer Charge values from B6 and E6 as clustered columns on the primary axis, and their Energy Charge values from B11 and E11 as a line with markers on the secondary axis. Name the c... | The four rates are split across nonadjacent Small and Medium Commercial columns and differ greatly in scale. Building a combo chart requires explicitly adding the monthly-charge and energy-charge series, assigning different chart types, and placing the energy-rate series on the secondary axis. | Utilities!G4:N18 contains a combo chart named "Commercial Rate Structure" titled "Commercial Rate Structure Comparison", with categories ["Small Commercial", "Medium Commercial"], primary-axis clustered-column series "Monthly Customer Charge" = [7.5, 110], secondary-axis line-with-markers series "Energy Charge" = [0.04... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const smallHeader = sheet.getRange("B4");
const mediumHeader = sheet.getRange("E4");
const smallMonthly = sheet.getRange("B6");
const mediumMonthly = sheet.getRange("E6");
const smallEnergy = sheet.getRange... | null | outputs/train/40cfd34af56fa6cd010452f00720d53f220494fa9b6cf2873351afdf33e05b84/gold.xlsx | Utilities!G4:N18 contains a combo chart named "Commercial Rate Structure" titled "Commercial Rate Structure Comparison", with categories ["Small Commercial", "Medium Commercial"], primary-axis clustered-column series "Monthly Customer Charge" = [7.5, 110], secondary-axis line-with-markers series "Energy Charge" = [0.04... | 2026-09-08T09:10:04.249008+00:00 | Chart | 1,080 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n const inputs = sheet.getRange(\"B4:E11\");\n inputs.load(\"values\");\n await context.sync();\n\n for (const c of charts.item... | 40cfd34af56fa6cd010452f00720d53f220494fa9b6cf2873351afdf33e05b84 | workbooks/Backward_EnronFuse.1016-File.xlsx | 3987ca7fe061a1715f566af4cab3dc093c8c38603954144abd47f784647056dd | 3b6fe68d3d2fc67293529ba4e9702a3cb46af945d9c5f5659ad196747c5a3327 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1010-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1010-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, create a formula-linked comparison block in G25:J27. In G25:J25 enter “Charge Type”, “Small Commercial”, “Medium Commercial”, and “Geometric Mean Benchmark”. Use “Monthly Customer Charge” and “Energy Charge” in G26:G27. H26:I27 must use absolute-reference formulas linked to B6, E6, B11, and E11, and J26:J... | The raw charges are in nonadjacent cells B6, E6, B11, and E11. A formula-linked table can feed the two column series, while an adjacent GEOMEAN formula provides a scale-neutral benchmark that must be added as a third series and changed to a line. A logarithmic axis is appropriate because the values range from 0.0232 to... | Utilities!G25:J27 contains headers ["Charge Type","Small Commercial","Medium Commercial","Geometric Mean Benchmark"], row labels ["Monthly Customer Charge","Energy Charge"], H26="=$B$6" yielding 7.5, I26="=$E$6" yielding 110, H27="=$B$11" yielding 0.044, I27="=$E$11" yielding 0.0232, J26="=GEOMEAN(H26:I26)" yielding ap... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const block = sheet.getRange("G25:J27");
block.formulas = [
["Charge Type", "Small Commercial", "Medium Commercial", "Geometric Mean Benchmark"],
["Monthly Customer Charge", "=$B$6", "=$E$6", "=GEOMEAN(H2... | null | outputs/train/584537eadba071160f11643acf13f794a40d681fdad97f42bc328c79719339dd/gold.xlsx | Utilities!G25:J27 contains headers ["Charge Type","Small Commercial","Medium Commercial","Geometric Mean Benchmark"], row labels ["Monthly Customer Charge","Energy Charge"], H26="=$B$6" yielding 7.5, I26="=$E$6" yielding 110, H27="=$B$11" yielding 0.044, I27="=$E$11" yielding 0.0232, J26="=GEOMEAN(H26:I26)" yielding ap... | 2026-09-08T09:11:07.411006+00:00 | Chart | 774 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n const block = sheet.getRange(\"G25:J27\");\n block.values = [\n [\"Charge Type\", \"Small Commercial\... | 584537eadba071160f11643acf13f794a40d681fdad97f42bc328c79719339dd | workbooks/Backward_EnronFuse.1010-File.xlsx | 606e05d965ed602272c59e288dd16cceb459220812ef14788a0f47a71d79a5b1 | f7d1f0e8957eedb3217f85d7f57e97fe3c3e7f60177381a2d075d2037af3a548 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1010-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1010-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, create a linked break-even analysis using the customer charges in B6 and E6 and energy charges in B11 and E11, ignoring the Medium Commercial demand charge. Build a scenario table with Usage (kWh), Small Commercial Bill, Medium Commercial Bill, and Lower-Cost Class. Include 1,000, 3,000, and 5,000 kWh plu... | The break-even point is the intersection of two linear bill equations based on B6/B11 and E6/E11. The corrected code propagates row-relative formulas with copyFrom, explicitly constructs all three chart series from their required X/Y ranges, and verifies that both bill series received a linear trendline. | Utilities!G4:J8 displays [["Usage (kWh)","Small Commercial Bill","Medium Commercial Bill","Lower-Cost Class"],[1000.00,51.50,133.20,"Small Commercial"],[3000.00,139.50,179.60,"Small Commercial"],[4927.88,224.33,224.33,"Equal"],[5000.00,227.50,226.00,"Medium Commercial"]]. G7 contains =($E$6-$B$6)/($B$11-$E$11). Utiliti... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
// Create the linked scenario analysis.
sheet.getRange("G4:J4").values = [[
"Usage (kWh)",
"Small Commercial Bill",
"Medium Commercial Bill",
"Lower-Cost Class"
]];
sheet.getRange("G5").value... | null | outputs/train/f19ebc9ba4948aa3d67dda5c71cc4507956044bc21d8a13820563d493a08fa63/gold.xlsx | Utilities!G4:J8 displays [["Usage (kWh)","Small Commercial Bill","Medium Commercial Bill","Lower-Cost Class"],[1000.00,51.50,133.20,"Small Commercial"],[3000.00,139.50,179.60,"Small Commercial"],[4927.88,224.33,224.33,"Equal"],[5000.00,227.50,226.00,"Medium Commercial"]]. G7 contains =($E$6-$B$6)/($B$11-$E$11). Utiliti... | 2026-09-08T09:11:11.264989+00:00 | Chart | 775 | 0 | 5 | 4 | 2 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
... | f19ebc9ba4948aa3d67dda5c71cc4507956044bc21d8a13820563d493a08fa63 | workbooks/Backward_EnronFuse.1010-File.xlsx | 606e05d965ed602272c59e288dd16cceb459220812ef14788a0f47a71d79a5b1 | 77f13f42557aa646cf1a486c40d70391c43e2783247804f7565d8e9dfab5a02d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.103-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.103-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a line chart showing O&M in $/kw-yr for periods 1 through 9, add a linear trendline, title it “O&M Cost Trend,” and place it to the right of the existing data. | The O&M row on Sheet1 contains a constant value of 9 for periods 1–9. A new line chart can use those actual period/value pairs, and the chart series’ trendline collection can add the requested linear trendline. | Sheet1 contains a line-marker chart named "OM_Cost_Trend" at O3:W18, titled "O&M Cost Trend", with categories [1,2,3,4,5,6,7,8,9], one series named "O&M" containing [9,9,9,9,9,9,9,9,9], and one linear trendline. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const chart = sheet.charts.add(
Excel.ChartType.lineMarkers,
sheet.getRange("A10:M10"),
Excel.ChartSeriesBy.rows
);
chart.name = "OM_Cost_Trend";
chart.setPosition("O3", "W18");
chart.title.text =... | null | outputs/train/594aa7c26e5addf7a64113ceceebfcc2eb58222e73e8623815b967c21c92bc97/gold.xlsx | Sheet1 contains a line-marker chart named "OM_Cost_Trend" at O3:W18, titled "O&M Cost Trend", with categories [1,2,3,4,5,6,7,8,9], one series named "O&M" containing [9,9,9,9,9,9,9,9,9], and one linear trendline.
| 2026-09-08T09:12:35.162708+00:00 | Chart | 1,846 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build a compact chart source to the right of the existing data, linked to the original O&M row.\n const source = sheet.getRange(\"O3:P12\");\n source.values = [\n [\"Period\", \"O&M ($/... | 594aa7c26e5addf7a64113ceceebfcc2eb58222e73e8623815b967c21c92bc97 | workbooks/Backward_EnronFuse.103-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | 3e3383c8b93bb0f938e029898cbf5d8f77a1c4c132fb354eb6f1f386be9ebc10 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1021-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1021-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, build a demand-charge sensitivity chart for the Medium Commercial plan. Plot the demand charge at peak demands of 20, 25, 27.44, 30, and 35 kW using the rule that only demand above 25 kW is charged at $3.60 per kW. Add a vertical series marking the 25 kW threshold and a separate one-point series highlight... | The Utilities sheet gives Medium Commercial demand pricing of $3.60 per kW above 25 kW and Example 1's peak demand of 27.44 kW, whose existing demand-charge formula evaluates to $8.784. A scatter chart with a modeled charge curve, a vertical threshold, and a separately added actual-example series makes the task substan... | Utilities!G4:H9 = [["Peak Demand (kW)","Modeled Demand Charge ($)"],[20,0],[25,0],[27.44,8.784],[30,18],[35,36]]; chart "MediumCommercialDemandSensitivity" at K4:S18 is an XY scatter-lines chart titled "Medium Commercial Demand Charge Sensitivity" with series "Modeled Demand Charge" using X values [20,25,27.44,30,35] a... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const examplePeakCell = sheet.getRange("C16");
const exampleDemandChargeCell = sheet.getRange("E16");
examplePeakCell.load("values");
exampleDemandChargeCell.load("values");
const oldChart = sheet.charts.ge... | null | outputs/train/f46bd1eb8973aa99b391fb15f06a3fc3e45307b5ff61b4c0ab3399837faf7d08/gold.xlsx | Utilities!G4:H9 = [["Peak Demand (kW)","Modeled Demand Charge ($)"],[20,0],[25,0],[27.44,8.784],[30,18],[35,36]]; chart "MediumCommercialDemandSensitivity" at K4:S18 is an XY scatter-lines chart titled "Medium Commercial Demand Charge Sensitivity" with series "Modeled Demand Charge" using X values [20,25,27.44,30,35] a... | 2026-09-08T09:12:38.241750+00:00 | Chart | 1,386 | 1 | 5 | 2 | 1 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n // Build the sensiti... | f46bd1eb8973aa99b391fb15f06a3fc3e45307b5ff61b4c0ab3399837faf7d08 | workbooks/Backward_EnronFuse.1021-File.xlsx | 024756f5ca383361d80e3bff359d54fc5772fd4c9ad8f394e917d874407fce7c | 2678bcb38cc6e93be8b24bc5e415dacddd15e625637b931f248e90bb70f8c338 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1014-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1014-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, remove every existing chart whose name starts with “Example1PeakTotal,” then create exactly one replacement chart named “Example1PeakTotal” at G4:N20. Build P4:R6 as a linked helper table: headers “Metric,” “Actual,” and “Share of combined”; rows Peak and Total; Actual formulas pointing to C16 and C17; an... | The harder task still centers on adding a trendline, but now requires idempotent cleanup of possible duplicate-name variants, a formula-driven helper table derived from C16:C17, two differently scaled series, a secondary axis, distinct series formatting, and verification that only the Actual series receives a trendline... | Utilities!P4:R6 contains headers Metric, Actual, Share of combined; P5:P6 contains Peak and Total; Q5:Q6 has formulas =C16 and =C17 evaluating to 27.44 and 7138; R5:R6 has formulas =Q5/SUM($Q$5:$Q$6) and =Q6/SUM($Q$5:$Q$6), displayed as 0.4% and 99.6%. Utilities contains exactly one chart whose name starts with "Exampl... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const charts = sheet.charts;
// Remove the target and any duplicate-name variants left by earlier runs.
charts.load("items/name");
await context.sync();
for (const existing of charts.items) {
if (exist... | null | outputs/train/991b20c7e65e2911a94c0859100c4ce032549db3a893bcd01af688bc21e31097/gold.xlsx | Utilities!P4:R6 contains headers Metric, Actual, Share of combined; P5:P6 contains Peak and Total; Q5:Q6 has formulas =C16 and =C17 evaluating to 27.44 and 7138; R5:R6 has formulas =Q5/SUM($Q$5:$Q$6) and =Q6/SUM($Q$5:$Q$6), displayed as 0.4% and 99.6%. Utilities contains exactly one chart whose name starts with "Exampl... | 2026-09-08T09:13:33.893701+00:00 | Chart | 979 | 1 | 5 | 4 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n charts.items.forEach(c => { if (c.name && c.name.startsWith(\"Example1PeakTotal\")) c.delete(); });\n ... | 991b20c7e65e2911a94c0859100c4ce032549db3a893bcd01af688bc21e31097 | workbooks/Backward_EnronFuse.1014-File.xlsx | 3987ca7fe061a1715f566af4cab3dc093c8c38603954144abd47f784647056dd | 8c035a5db2056f23879f4b8c20d41036bb84ef5f29d888a857c1446a04039264 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1023-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1023-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Utilities, build a Medium Commercial peak-demand sensitivity table in G21:I27 using Example 1’s 7,138 units of usage. Test peaks of 20, 25, 27.44, 30, 35, and 40 kW; calculate the demand charge and total bill for each using the existing tariff cells. Create a combo chart from the table with demand charge as columns ... | The task derives a six-scenario sensitivity table from the Medium Commercial tariff cells and Example 1 usage, then creates and customizes a mixed-type, dual-axis chart. The unsupported axis-title calls are omitted because axis titles are not requested; assigning the Total Bill series to the secondary axis creates the ... | Utilities!G21:I27 contains [["Peak kW","Demand Charge","Total Bill"],[20,0,275.6016],[25,0,275.6016],[27.44,8.784,284.3856],[30,18,293.6016],[35,36,311.6016],[40,54,329.6016]]. Utilities also contains a chart named "MediumCommercialSensitivityChart" at G4:N18 titled "Medium Commercial Bill Sensitivity at 7,138 Units"; ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
const charts = sheet.charts;
const oldChart = charts.getItemOrNullObject("MediumCommercialSensitivityChart");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart... | null | outputs/train/4f3249049c5c294cbb40d927708d59dc3f107741b37675f2908c66308c04956a/gold.xlsx | Utilities!G21:I27 contains [["Peak kW","Demand Charge","Total Bill"],[20,0,275.6016],[25,0,275.6016],[27.44,8.784,284.3856],[30,18,293.6016],[35,36,311.6016],[40,54,329.6016]]. Utilities also contains a chart named "MediumCommercialSensitivityChart" at G4:N18 titled "Medium Commercial Bill Sensitivity at 7,138 Units"; ... | 2026-09-08T09:17:11.941682+00:00 | Chart | 1,486 | 0 | 5 | 4 | 2 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const ws = context.workbook.worksheets.getItem(\"Utilities\");\n const r = ws.getRange(\"A4:E28\");\n r.load([\"values\",\"formulas\"]);\n await context.sync();\n console.log(JSON.stringify({values:r.values, formulas:r.formulas}));\n await context.sync();\... | 4f3249049c5c294cbb40d927708d59dc3f107741b37675f2908c66308c04956a | workbooks/Backward_EnronFuse.1023-File.xlsx | 3e8dc8cd071db31ffaa014ecbc81912207106117fa99df0eae59be728ce8a1bc | ed1801af580f67b54e2d318d11514467be52fde4607ecd84553490e25dc8a01a | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.103-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.103-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a line-with-markers chart named “NetOperatingCashFlowChart” that compares the three 3-year period blocks (Periods 1–3, 4–6, and 7–9) on a common Contract Year 1–3 axis. For each period, calculate net operating cash flow before debt service as Marketing revenues minus Fin. energy pmts minus O&M, treati... | The sheet’s nine periods form three separate three-year blocks in C:E, G:I, and K:M. Creating the requested comparison requires calculating and reshaping net operating cash flow into aligned series, then explicitly adding each series and a zero benchmark to a new chart. The JavaScript code removes the invalid TypeScrip... | Sheet1 contains a line-with-markers chart named NetOperatingCashFlowChart at O3:W20, titled “Net Operating Cash Flow by Contract Year ($/kw-yr)”, with a bottom legend and Contract Year categories [1,2,3]. Its series are Periods 1–3 = [41,-9,-59], Periods 4–6 = [-9,-59,-109], Periods 7–9 = [91,41,-9], and Zero benchmark... | await Excel.run(async (context) => {
const workbook = context.workbook;
const sheet = workbook.worksheets.getItem("Sheet1");
const source = sheet.getRange("C7:M10");
source.load("values");
const oldChart = sheet.charts.getItemOrNullObject("NetOperatingCashFlowChart");
oldChart.load("isNullObject");
let ... | null | outputs/train/3f8d7b1948fe113ce5d1ddb4a0a58a4c0c924c1fb0bb040f5ad538c6e5eb63f0/gold.xlsx | Sheet1 contains a line-with-markers chart named NetOperatingCashFlowChart at O3:W20, titled “Net Operating Cash Flow by Contract Year ($/kw-yr)”, with a bottom legend and Contract Year categories [1,2,3]. Its series are Periods 1–3 = [41,-9,-59], Periods 4–6 = [-9,-59,-109], Periods 7–9 = [91,41,-9], and Zero benchmark... | 2026-09-08T09:17:12.871381+00:00 | Chart | 1,845 | 0 | 5 | 3 | 1 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build a compact source table outside the chart area.\n const source = sheet.getRange(\"AA3:AE6\");\n source.values = [\n [\"Contract Year\", \"Periods 1–3\", \"Periods 4–6\", \"Periods ... | 3f8d7b1948fe113ce5d1ddb4a0a58a4c0c924c1fb0bb040f5ad538c6e5eb63f0 | workbooks/Backward_EnronFuse.103-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | f423babf0c32d1fc6c590efe6f7c76ca9ab5a33504053c5e1f23fea853370ac5 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1036-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1036-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a combo chart on 'Shortfall Calculation' using February through July 2001, without adding a helper table. Show Avg Mkt Price and Shortfall as clustered columns on the primary $/MWh axis, and overlay MW Need in CA as a line on a secondary MW axis. Keep the negative shortfall values below zero, title the chart 'Ca... | Rows 3, 4, and 6 contain three nonadjacent series with very different scales. Building a useful combo chart requires adding each series explicitly, assigning different chart types, and placing MW Need on a secondary axis without creating a helper table. | A new combo chart named 'CaliforniaNeedPriceShortfall' on 'Shortfall Calculation', positioned at A18:I34 and titled 'California Need, Market Price, and Shortfall'. Its categories are B1:G1 (February through July 2001). It has three explicitly added series: 'Avg Mkt Price ($/Mwh)' as clustered columns on the primary axi... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const chartName = "CaliforniaNeedPriceShortfall";
const oldChart = sheet.charts.getItemOrNullObject(chartName);
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) ... | null | outputs/train/8c3cf027fca16cac628753afdb03ea72af18605c295171b9d07cd8f30eecf1a8/gold.xlsx | A new combo chart named 'CaliforniaNeedPriceShortfall' on 'Shortfall Calculation', positioned at A18:I34 and titled 'California Need, Market Price, and Shortfall'. Its categories are B1:G1 (February through July 2001). It has three explicitly added series: 'Avg Mkt Price ($/Mwh)' as clustered columns on the primary axi... | 2026-09-08T09:19:09.238936+00:00 | Chart | 2,202 | 0 | 5 | 2 | 1 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const charts = sheet.charts;\n\n charts.load(\"items/name\");\n await context.sync();\n for (const c of charts.items) {\n if (c.name === \"CaliforniaNeedMarketPriceShortfall\... | 8c3cf027fca16cac628753afdb03ea72af18605c295171b9d07cd8f30eecf1a8 | workbooks/Backward_EnronFuse.1036-File.xlsx | 926cf70825679ded0203533743e6c9be53feea405e6e025cca99d2ab71463873 | 768ba0e651edce281979c5bfb44bfa4d0802dbb13bd1d39ae2ef178de718edcf | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.104-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.104-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a line chart with markers comparing Marketing revenues, Fin. energy pmts, Debt service/fixed pmt, and O&M for years 1, 2, and 3. Title it “Years 1–3 Revenue and Cost Comparison” and place it from O3 to W18. | The year categories are in Sheet1!C5:E5, while the four financial series are in C7:E10. The chart can be initialized from the Marketing revenues row, then the remaining series can be added and all series linked with sheet-qualified categoryFormula and formula properties. | Sheet1 contains a line-with-markers chart named 'Year1to3CostComparison' positioned at O3:W18, titled 'Years 1–3 Revenue and Cost Comparison', with categories [1, 2, 3] and series: Marketing revenues [100, 50, blank], Fin. energy pmts [50, 50, 50], Debt service/fixed pmt [40, 40, 40], and O&M [9, 9, 9]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const existingChart = sheet.charts.getItemOrNullObject("Year1to3CostComparison");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
existingChart.delete();
awa... | null | outputs/train/180d01989cc213d78b06f8f5028207f2fa888a2efe818bfbe82c877406bd8888/gold.xlsx | Sheet1 contains a line-with-markers chart named 'Year1to3CostComparison' positioned at O3:W18, titled 'Years 1–3 Revenue and Cost Comparison', with categories [1, 2, 3] and series: Marketing revenues [100, 50, blank], Fin. energy pmts [50, 50, 50], Debt service/fixed pmt [40, 40, 40], and O&M [9, 9, 9].
| 2026-09-08T09:19:46.176733+00:00 | Chart | 2,406 | 2 | 5 | 2 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const charts = sheet.charts;\n\n // Remove an earlier copy of this chart, if present.\n charts.load(\"items/name\");\n await context.sync();\n for (const existing of charts.items) {\n if (... | 180d01989cc213d78b06f8f5028207f2fa888a2efe818bfbe82c877406bd8888 | workbooks/Backward_EnronFuse.104-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | 76f7153b9a1efac2b37913f0f4c872e3243c557a35d610b1b3efd6957099dec4 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1023-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1023-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Utilities sheet, create a four-period customer-charge analysis while leaving A4:E28 unchanged. Build the base table in G22:I26: put “Charge Period” in G22, make H22 and I22 formulas linked to the existing customer-class names in B4 and E4, and list Monthly, Quarterly, Semiannual, and Annual in G23:G26. In H23:I2... | Utilities!B4/E4 contain the customer-class names and B6/E6 contain their monthly charges. The task derives four billing periods in a linked helper layout, charts the two contiguous class columns, and then uses the series collection to append a third series from a separated variance block. The new series is fully config... | Utilities!G22:I26 contains [["Charge Period","Small Commercial","Medium Commercial"],["Monthly",7.5,110],["Quarterly",22.5,330],["Semiannual",45,660],["Annual",90,1320]], with H22 =$B$4, I22 =$E$4, H23:H26 = [=$B$6,=$B$6*3,=$B$6*6,=$B$6*12], and I23:I26 = [=$E$6,=$E$6*3,=$E$6*6,=$E$6*12]. Utilities!J22:J26 is blank. Ut... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Utilities");
// Build the linked base table.
sheet.getRange("G22:G26").values = [
["Charge Period"],
["Monthly"],
["Quarterly"],
["Semiannual"],
["Annual"]
];
sheet.getRange("H22:I26").formulas = [
... | null | outputs/train/87cea7b83d013bbbb3f1a252f9fcb5f309f04fa659341a73816b91890f10efcd/gold.xlsx | Utilities!G22:I26 contains [["Charge Period","Small Commercial","Medium Commercial"],["Monthly",7.5,110],["Quarterly",22.5,330],["Semiannual",45,660],["Annual",90,1320]], with H22 =$B$4, I22 =$E$4, H23:H26 = [=$B$6,=$B$6*3,=$B$6*6,=$B$6*12], and I23:I26 = [=$E$6,=$E$6*3,=$E$6*6,=$E$6*12]. Utilities!J22:J26 is blank. Ut... | 2026-09-08T09:20:48.750269+00:00 | Chart | 1,488 | 2 | 5 | 6 | 2 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Utilities\");\n\n // Build the four-period charge analysis, leaving A4:E28 untouched.\n sheet.getRange(\"G22:G26\").values... | 87cea7b83d013bbbb3f1a252f9fcb5f309f04fa659341a73816b91890f10efcd | workbooks/Backward_EnronFuse.1023-File.xlsx | 3e8dc8cd071db31ffaa014ecbc81912207106117fa99df0eae59be728ce8a1bc | 5f28a6736c016d2267e13299fdd51621005ff5d9404ec66873bafe0a966fee32 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1031-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1031-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', populate Rolling Spending in B10:G10 as a cumulative total of Monthly Spending, starting with B10 equal to B8. Add the label '3-Month Avg Spending' in A11 and calculate a trailing three-month average of Monthly Spending in B11:G11, using #N/A for February and March so the line begins in Apri... | The workbook has monthly spending in row 8 and a blank Rolling Spending row. This task derives both a cumulative series and a trailing-average series with intentional #N/A gaps, then explicitly adds and configures both series in a three-series combo chart with separate scales. | Shortfall Calculation!B10:G10 contains formulas [=B8, =B10+C8, =C10+D8, =D10+E8, =E10+F8, =F10+G8] and values [-963600000, -2104726875, -3477959531.25, -5046613757.8125, -6896675045.625, -9071229097.44375]. Shortfall Calculation!A11 is "3-Month Avg Spending"; B11:G11 contains formulas [=NA(), =NA(), =AVERAGE(B8:D8), =A... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const rollingRange = sheet.getRange("B10:G10");
rollingRange.formulas = [[
"=B8",
"=B10+C8",
"=C10+D8",
"=D10+E8",
"=E10+F8",
"=F10+G8"
]];
sheet.getRange("A11").values = ... | null | outputs/train/ed446c0fe08700b1608bf3f9b50921ce31a699ea757f7875a87eb1c17aebc756/gold.xlsx | Shortfall Calculation!B10:G10 contains formulas [=B8, =B10+C8, =C10+D8, =D10+E8, =E10+F8, =F10+G8] and values [-963600000, -2104726875, -3477959531.25, -5046613757.8125, -6896675045.625, -9071229097.44375]. Shortfall Calculation!A11 is "3-Month Avg Spending"; B11:G11 contains formulas [=NA(), =NA(), =AVERAGE(B8:D8), =A... | 2026-09-08T09:21:12.687802+00:00 | Chart | 1,947 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Popul... | ed446c0fe08700b1608bf3f9b50921ce31a699ea757f7875a87eb1c17aebc756 | workbooks/Backward_EnronFuse.1031-File.xlsx | be458037c2ee7aca4e25103c1051f402c763e776248297da08f4bba4ad370536 | 6894ff8c40998018836e3fdaa9c740531bd5b8c6e46bb5a3f699e08a4634be72 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1026-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1026-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Build a visible Pareto-style helper table on Sheet1 in B25:E30 from the positive development outflows listed in B9:B13 and G9:G13. Use the headers “Rank,” “Category,” “Outflow ($000s),” and “Cumulative Share,” sort the costs from largest to smallest, number them 1–5, and calculate cumulative share with formulas based o... | The task extracts nonadjacent source data, filters and sorts the outflows, writes formula-driven cumulative shares, and charts only that transformed metric. The corrected chart configuration uses the valid top data-label position and separate synchronization points before adding exactly one linear trendline. | Sheet1!B25:E30 contains headers ["Rank","Category","Outflow ($000s)","Cumulative Share"] and rows [[1,"Unit Costs",7600,69.7%],[2,"Land",1100,79.8%],[3,"Carrying Costs",900,88.1%],[4,"Common Amenities",700,94.5%],[5,"Improvements",600,100.0%]], with E26:E30 calculated by cumulative-share formulas and formatted "0.0%". ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const categories = sheet.getRange("B9:B13");
const outflows = sheet.getRange("G9:G13");
categories.load("values");
outflows.load("values");
const oldChart = sheet.charts.getItemOrNullObject(
"Development ... | null | outputs/train/87c7426da8a6791e50acdca378f52b29569dbf2d536afe6beed126031e206de8/gold.xlsx | Sheet1!B25:E30 contains headers ["Rank","Category","Outflow ($000s)","Cumulative Share"] and rows [[1,"Unit Costs",7600,69.7%],[2,"Land",1100,79.8%],[3,"Carrying Costs",900,88.1%],[4,"Common Amenities",700,94.5%],[5,"Improvements",600,100.0%]], with E26:E30 calculated by cumulative-share formulas and formatted "0.0%". ... | 2026-09-08T09:21:30.844506+00:00 | Chart | 1,642 | 0 | 5 | 7 | 2 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const charts = sheet.char... | 87c7426da8a6791e50acdca378f52b29569dbf2d536afe6beed126031e206de8 | workbooks/Backward_EnronFuse.1026-File.xlsx | 20307b1867a330f2b28225c1e073cbf57ade8e0918b02a5b0bc8eae0095fb8a9 | b64b93942bc170278b086eef1b8aa0d6bd9078ec2b6d830f3e1a7652a54e1b59 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1034-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1034-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', build a decision-support combo chart for February through July 2001 without changing A1:I16. Show MW Need in CA as clustered columns on the primary axis, then show Avg Mkt Price, Shortfall Magnitude (the absolute value of Shortfall), and a 10% Stress Shortfall Magnitude as lines on the secon... | This requires deriving two new scenario rows from the negative Shortfall values, then explicitly adding four noncontiguous series to a combo chart. The large MW Need values must use clustered columns on the primary axis, while the three $/MWh measures use lines on the secondary axis. | On 'Shortfall Calculation', J22:P24 contains: row 22 = ['Scenario', 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 2001-06-01, 2001-07-01]; row 23 = ['Shortfall Magnitude ($/Mwh)', 150, 157.5, 165.375, 173.64375, 182.3259375, 191.442234375]; row 24 = ['10% Stress Shortfall Magnitude ($/Mwh)', 165, 173.25, 181.9125, 19... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
// Build the supporting scenario table without altering the original model.
sheet.getRange("J22").values = [["Scenario"]];
sheet.getRange("K22:P22").formulas = [[
"=B1", "=C1", "=D1", "=E1", "=F... | null | outputs/train/0df306e4d62219a5d1e82e9ed09ab6264665cfe49d8cc814a0814ffff420ec30/gold.xlsx | On 'Shortfall Calculation', J22:P24 contains: row 22 = ['Scenario', 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 2001-06-01, 2001-07-01]; row 23 = ['Shortfall Magnitude ($/Mwh)', 150, 157.5, 165.375, 173.64375, 182.3259375, 191.442234375]; row 24 = ['10% Stress Shortfall Magnitude ($/Mwh)', 165, 173.25, 181.9125, 19... | 2026-09-08T09:22:19.348082+00:00 | Chart | 2,100 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Build the supporting scenario table without changing A1:I16.\n const support = sheet.getR... | 0df306e4d62219a5d1e82e9ed09ab6264665cfe49d8cc814a0814ffff420ec30 | workbooks/Backward_EnronFuse.1034-File.xlsx | 926cf70825679ded0203533743e6c9be53feea405e6e025cca99d2ab71463873 | b3ea23c5103a805e06178537cb38acb0e7914c8e6fd1220612c7f8a353baeced | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1033-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1033-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', create a bubble chart in A18:I34 titled 'Monthly California Shortfall Exposure'. For February through July 2001, use Avg Mkt Price as the x-value, the absolute Monthly Spending in $ billions rounded to 6 decimals as the y-value, and MW Need in CA as the bubble size. Make each month its own s... | A bubble chart cannot infer this layout from the nonadjacent source rows: average price supplies x-values, monthly spending must be converted to positive billions for y-values, and MW Need supplies bubble sizes. Making every month a separate legend entry requires adding and configuring six individual series. | On 'Shortfall Calculation', hidden range J1:M7 contains [['Month','Avg Mkt Price','Spending ($bn)','MW Need'],['Feb 2001',150,0.9636,8800],['Mar 2001',157.5,1.141127,9925],['Apr 2001',165.375,1.373233,11375],['May 2001',173.64375,1.568654,12375],['Jun 2001',182.3259375,1.850061,13900],['Jul 2001',191.44223437500003,2.1... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const helper = sheet.getRange("J1:M7");
helper.values = [
["Month", "Avg Mkt Price", "Spending ($bn)", "MW Need"],
["Feb 2001", "", "", ""],
["Mar 2001", "", "", ""],
["Apr 2001", "", ... | null | outputs/train/40c39aef5eb53285d829d6a05e768614de3c04e289f68ec4cb3faaa6f6a7e8de/gold.xlsx | On 'Shortfall Calculation', hidden range J1:M7 contains [['Month','Avg Mkt Price','Spending ($bn)','MW Need'],['Feb 2001',150,0.9636,8800],['Mar 2001',157.5,1.141127,9925],['Apr 2001',165.375,1.373233,11375],['May 2001',173.64375,1.568654,12375],['Jun 2001',182.3259375,1.850061,13900],['Jul 2001',191.44223437500003,2.1... | 2026-09-08T09:22:39.960219+00:00 | Chart | 2,049 | 2 | 5 | 3 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Build one supporting row per month: label, x value, y value in $ billions, bubble size.\n const support = sheet.getRange(\"J1:M7\");\n support.values = [\n [\"Month\", \"... | 40c39aef5eb53285d829d6a05e768614de3c04e289f68ec4cb3faaa6f6a7e8de | workbooks/Backward_EnronFuse.1033-File.xlsx | be458037c2ee7aca4e25103c1051f402c763e776248297da08f4bba4ad370536 | 98b060c0c01b024aca87d437dceae042ff6674099e27883975ad93e478df1efa | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1038-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1038-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Shortfall Calculation sheet, create a line chart of Monthly Spending for February through July 2001 to analyze how the spending decline is accelerating. Add a third-order polynomial trendline, show its equation and R² on the chart, and extend it two months forward. Title the chart “Monthly Spending Acceleration ... | Monthly Spending in B8:G8 becomes increasingly negative across the dates in B1:G1, making a cubic trendline with a two-period projection useful for analyzing acceleration. The chart is built from only the required row, and the trendline is created and synchronized before each advanced setting is applied separately. The... | A new chart named “MonthlySpendingForecast” at Shortfall Calculation!J2:R19, titled “Monthly Spending Acceleration and 2-Month Forecast,” with axes titled “Month” and “Monthly Spending ($).” It contains one series named “Monthly Spending” with February–July 2001 values -963600000, -1141126875, -1373232656.25, -15686542... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
// Use only the spending values so no unrelated series need to be deleted.
const chart = sheet.charts.add(
Excel.ChartType.lineMarkers,
sheet.getRange("B8:G8"),
Excel.ChartSeriesBy.rows
... | null | outputs/train/eb1429a5a99436c60bb12ebf8973dfa85b28791ebf083f0586db32dc9b281230/gold.xlsx | A new chart named “MonthlySpendingForecast” at Shortfall Calculation!J2:R19, titled “Monthly Spending Acceleration and 2-Month Forecast,” with axes titled “Month” and “Monthly Spending ($).” It contains one series named “Monthly Spending” with February–July 2001 values -963600000, -1141126875, -1373232656.25, -15686542... | 2026-09-08T09:23:54.691194+00:00 | Chart | 2,305 | 2 | 5 | 5 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n for (const c of charts.items) {\n if (c.name === \"Monthly Spending Acceleration Chart\"... | eb1429a5a99436c60bb12ebf8973dfa85b28791ebf083f0586db32dc9b281230 | workbooks/Backward_EnronFuse.1038-File.xlsx | c786f14a1f5284cc4f896ac41090b7fa05af094762623c5cd2c3a8986b7a778b | 04e607be8c009779cac1a3fcf199eaba817cc907c2ffe909aa88fa8cc967448e | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1040-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1040-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Shortfall Calculation sheet, create a scatter chart below the data that plots Monthly Spending on the vertical axis against MW Need in CA on the horizontal axis for February through July. Add both a linear fit and a second-order polynomial fit, show the equation and R-squared value for each fit, and name them “L... | Rows 3 and 8 provide six paired observations of California MW need and monthly spending. A scatter chart comparing linear and quadratic fits requires creating a new chart and adding two distinct trendlines, including configuring the polynomial order and fit-statistic labels. | Shortfall Calculation!A18:I35 contains a new XY scatter chart named "Spending vs MW Need" titled "Monthly Spending vs. California MW Need". Its single series, "Feb–Jul 2001", has x-values from B3:G3 (8800, 9925, 11375, 12375, 13900, 15560) and y-values from B8:G8 (-963600000, -1141126875, -1373232656.25, -1568654226.56... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("Spending vs MW Need");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.... | null | outputs/train/a78971f033296591bf4f412b443788d12a8ce703057232e671428d9faba6aeae/gold.xlsx | Shortfall Calculation!A18:I35 contains a new XY scatter chart named "Spending vs MW Need" titled "Monthly Spending vs. California MW Need". Its single series, "Feb–Jul 2001", has x-values from B3:G3 (8800, 9925, 11375, 12375, 13900, 15560) and y-values from B8:G8 (-963600000, -1141126875, -1373232656.25, -1568654226.56... | 2026-09-08T09:24:38.580739+00:00 | Chart | 2,458 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n for (const c of charts.items) {\n if (c.name === \"MonthlySpendingVsCaliforniaMWNeed\") ... | a78971f033296591bf4f412b443788d12a8ce703057232e671428d9faba6aeae | workbooks/Backward_EnronFuse.1040-File.xlsx | c786f14a1f5284cc4f896ac41090b7fa05af094762623c5cd2c3a8986b7a778b | 9e03e0a1c4b13c9901a80fc611361028f8e0cf05bf34a98615da8634ee0ce2f0 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1044-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1044-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', create a bubble chart over I2:P16 showing how MW Need relates to Avg Mkt Price for February through July 2001. Make each month a separate legend entry and size its bubble by the absolute Monthly Spending in billions of dollars. Title it 'Demand, Price, and Monthly Spending', label the horizo... | A bubble chart requires each series to receive separate x-values, y-values, and bubble sizes. Using one single-point series per month makes the dates identifiable in the legend while connecting the workbook's MW Need, Avg Mkt Price, and Monthly Spending rows in one visualization. | A bubble chart named 'DemandPriceSpendingBubble' on 'Shortfall Calculation' at I2:P16, titled 'Demand, Price, and Monthly Spending', with right-side legend and six one-point series: Feb 2001 = (8800, 150, 0.9636), Mar 2001 = (9925, 157.5, 1.141126875), Apr 2001 = (11375, 165.375, 1.37323265625), May 2001 = (12375, 173.... | await Excel.run(async (context) => {
const workbook = context.workbook;
const sheet = workbook.worksheets.getItem("Shortfall Calculation");
// Prepare positive bubble sizes without changing the source calculation area.
let helper = workbook.worksheets.getItemOrNullObject("_BubbleChartData");
helper.load("isN... | null | outputs/train/cb277d2f4990f91d3d41ce9b26043629ea7c7511c61c973879901b460d26f57f/gold.xlsx | A bubble chart named 'DemandPriceSpendingBubble' on 'Shortfall Calculation' at I2:P16, titled 'Demand, Price, and Monthly Spending', with right-side legend and six one-point series: Feb 2001 = (8800, 150, 0.9636), Mar 2001 = (9925, 157.5, 1.141126875), Apr 2001 = (11375, 165.375, 1.37323265625), May 2001 = (12375, 173.... | 2026-09-08T09:24:55.808118+00:00 | Chart | 2,661 | 2 | 5 | 2 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const source = sheet.getRange(\"B1:G8\");\n source.load(\"values\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n\n for (const c of ... | cb277d2f4990f91d3d41ce9b26043629ea7c7511c61c973879901b460d26f57f | workbooks/Backward_EnronFuse.1044-File.xlsx | 37b9187afb56fa77e46fd64dbae68081b3e4f9056a2365dd6f134aae036a53bf | d1b0c862eb204f1a91a37ce735190f3e928e132efefd6b595887ba084b83ab13 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1058-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1058-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing the 1st Lien and 2nd Lien amounts from rows 8 and 9. Title it “Lien Principal Comparison,” add a linear trendline, and place the chart from F3 to M18. | Sheet1 contains the two lien principal amounts in C8:C9, labeled in A8:A9. A new clustered column chart can compare those values, and the specified trendline collection API is needed to add a linear trendline to its data series. | Sheet1 contains a clustered column chart named and titled "Lien Principal Comparison" at F3:M18, with categories "1st Lien" and "2nd Lien", values 475000 and 200000, and exactly 1 linear trendline. Console output: Sheet1!F3:M18: Lien Principal Comparison; 1st Lien=475000, 2nd Lien=200000; linear trendlines=1 | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sheet.getRange("C8:C9"),
Excel.ChartSeriesBy.columns
);
chart.name = "Lien Principal Comparison";
chart.title.text = "Lien Principal ... | null | outputs/train/716e32fa3549fc42153e7a254440325a5273a212aca79ca74019919aa1c626f2/gold.xlsx | Sheet1!F3:M18: Lien Principal Comparison; 1st Lien=475000, 2nd Lien=200000; linear trendlines=1
| 2026-09-08T09:25:43.174385+00:00 | Chart | 3,427 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Remove any prior version of this chart so the result is repeatable.\n const charts = sheet.charts;\n charts.load(\"items/name,title/text\");\n await context.sync();\n for (const existing... | 716e32fa3549fc42153e7a254440325a5273a212aca79ca74019919aa1c626f2 | workbooks/Backward_EnronFuse.1058-File.xlsx | 1277bea5865424ab2673821c1c3ce5b1d96e373c29acf144e01417434228bf34 | ff2167aec84a5a24d7e8580af6e29342978655e2e90c9b8f948c7c42a96b868f | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1059-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1059-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing the 1st Lien and 2nd Lien amounts from rows 8 and 9. Title it “Lien Balance Comparison,” hide the legend, and place it over cells E3:L18. | Sheet1 lists the 1st Lien and 2nd Lien amounts in A8:A9 and C8:C9. A clustered column chart can directly compare the two balances while using the lien labels as categories. | Sheet1 contains a clustered column chart named "LienBalancesChart" positioned at E3:L18, titled "Lien Balance Comparison", with no legend and two categories: 1st Lien = 475000 and 2nd Lien = 200000. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("LienBalancesChart");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.delete();
awa... | null | outputs/train/37e590c8a8fa90cba6c27e5854b5494712ddd316187abe5ecd6a84246ea4118a/gold.xlsx | Sheet1 contains a clustered column chart named "LienBalancesChart" positioned at E3:L18, titled "Lien Balance Comparison", with no legend and two categories: 1st Lien = 475000 and 2nd Lien = 200000.
| 2026-09-08T09:26:00.092195+00:00 | Chart | 3,475 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const source = sheet.getRange(\"A8:C9\");\n const chart = sheet.charts.add(Excel.ChartType.columnClustered, source, Excel.ChartSeriesBy.columns);\n chart.name = \"LienBalanceComparison\";\n ch... | 37e590c8a8fa90cba6c27e5854b5494712ddd316187abe5ecd6a84246ea4118a | workbooks/Backward_EnronFuse.1059-File.xlsx | 1d56e342105cb7ca94ca1179de6c4cf48d68bcd74449ff766d608fc6b622d1ae | c98425ef9c21b3c7b8a93008fd3b22502093a0fe97d6be662be5c93649fc09d4 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1048-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1048-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Shortfall Calculation' sheet, find the contiguous monthly date row and chart the matching values from the row labeled 'MW Need in CA' for the full available monthly period. Create a scatter chart with markers only and place it to the right of the table from I2 through P18. Title it 'California MW Need Trend'; t... | The revised solution keeps the content-based discovery, chart cleanup, axis formatting, markers-only series, and annotated trendline requirements, but removes all TypeScript-only annotations so the snippet is valid in a JavaScript runtime. | Shortfall Calculation contains a chart named 'California MW Need Trend' at I2:P18 with exactly one markers-only series named 'MW Need in CA', using February–July 2001 and values 8800, 9925, 11375, 12375, 13900, 15560; horizontal axis title 'Month' with number format 'mmm yyyy'; vertical axis title 'MW Need in CA', mini... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const used = sheet.getUsedRange();
used.load(["values", "rowIndex", "columnIndex"]);
await context.sync();
const values = used.values;
// Locate the data row from its label.
let needRow = -1;... | null | outputs/train/b2dec8f224da983c8209e3156a0bd069327c702997dbe2b2f115ba7a342a7ec4/gold.xlsx | Shortfall Calculation contains a chart named 'California MW Need Trend' at I2:P18 with exactly one markers-only series named 'MW Need in CA', using February–July 2001 and values 8800, 9925, 11375, 12375, 13900, 15560; horizontal axis title 'Month' with number format 'mmm yyyy'; vertical axis title 'MW Need in CA', mini... | 2026-09-08T09:26:03.754050+00:00 | Chart | 2,866 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const labels = sheet.getRange(\"A1:A16\");\n const datesToCheck = sheet.getRange(\"B1:I1\");\n labels.load(\"values\");\n datesToCheck.load(\"values\");\n await context.sync();... | b2dec8f224da983c8209e3156a0bd069327c702997dbe2b2f115ba7a342a7ec4 | workbooks/Backward_EnronFuse.1048-File.xlsx | b5628c5761b64c876b1be17843aba9f61ddb6ac91d1a8a7f65f54ce6da806a78 | 059f128be5a0ff8659dfdb20b091e9c9854b9d2717ab3d0388ae4d327d11e67d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1051-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1051-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', create a bubble chart from A18 to I34 that compares February through July 2001. Plot MW Need in CA on the horizontal axis and Avg Mkt Price ($/Mwh) on the vertical axis, with bubble size representing the absolute Monthly Spending in $ millions. Make each month a separate series so all six mo... | A bubble chart requires three different measures from nonadjacent rows: MW Need for x-position, Avg Mkt Price for y-position, and the magnitude of negative Monthly Spending for bubble size. Creating one series per month through the series collection is necessary to give February–July individual legend entries, while a ... | On 'Shortfall Calculation', hidden helper range A17:G17 contains 'Spending Magnitude ($M)' and the values 963.6, 1141.126875, 1373.23265625, 1568.6542265625, 1850.0612878125, 2174.55405181875. A bubble chart named 'NeedPriceSpendingBubble' is positioned at A18:I34, titled 'Need, Price, and Spending by Month', and conta... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const helper = sheet.getRange("A17:G17");
helper.formulas = [[
"Spending Magnitude ($M)",
"=ABS(B8)/1000000",
"=ABS(C8)/1000000",
"=ABS(D8)/1000000",
"=ABS(E8)/1000000",
"=ABS(... | null | outputs/train/36e84433607daa011e8197465f4e53023e86cfa6141873a919fa2c5eaf11a353/gold.xlsx | On 'Shortfall Calculation', hidden helper range A17:G17 contains 'Spending Magnitude ($M)' and the values 963.6, 1141.126875, 1373.23265625, 1568.6542265625, 1850.0612878125, 2174.55405181875. A bubble chart named 'NeedPriceSpendingBubble' is positioned at A18:I34, titled 'Need, Price, and Spending by Month', and conta... | 2026-09-08T09:26:18.704412+00:00 | Chart | 3,069 | 1 | 5 | 2 | 1 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Read the six months and their source values.\n const dates = sheet.getRange(\"B1:G1\");\n... | 36e84433607daa011e8197465f4e53023e86cfa6141873a919fa2c5eaf11a353 | workbooks/Backward_EnronFuse.1051-File.xlsx | 9d3a1fb3811ad9f6d70c25d64090c7e1126665d579c07a19159958f8f3c2612e | 68417d4fce1b6f062f72ee2b784617621ddcc3412e8d3502735174e5345a083b | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.104-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.104-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a worksheet named “Cash Flow Trend” by interpreting the layout of Sheet1 rather than assuming fixed source rows or contiguous period columns. Find the nine source columns by locating the numeric period labels 1 through 9 in row 5, and find the source rows by matching the exact labels “Marketing revenues,” “Fin. ... | The solution must interpret Sheet1’s layout by scanning row 5 for the noncontiguous period columns and column A for the four exact row labels. It then writes linked formulas with explicit blank handling, builds dependent fixed-cost and cumulative calculations, removes the chart’s automatically generated series one at a... | A new 'Cash Flow Trend' sheet whose A1:E10 has headers [['Period','Gross inflow','Fixed costs','Net cash','Cumulative net cash']] and data values [[1,150,49,101,101],[2,100,49,51,152],[3,50,49,1,153],[4,200,49,151,304],[5,150,49,101,405],[6,100,49,51,456],[7,100,49,51,507],[8,50,49,1,508],[9,0,49,-49,459]]. A2:A10 are ... | await Excel.run(async (context) => {
const workbook = context.workbook;
const source = workbook.worksheets.getItem("Sheet1");
const used = source.getUsedRange();
used.load(["values", "rowIndex", "columnIndex", "rowCount", "columnCount"]);
await context.sync();
const values = used.values;
const firstRow =... | null | outputs/train/02ed8e4baf167b0df60ed41c0995ae57c8f6bdc0e381344ba6e26b4a2b80395e/gold.xlsx | A new 'Cash Flow Trend' sheet whose A1:E10 has headers [['Period','Gross inflow','Fixed costs','Net cash','Cumulative net cash']] and data values [[1,150,49,101,101],[2,100,49,51,152],[3,50,49,1,153],[4,200,49,151,304],[5,150,49,101,405],[6,100,49,51,456],[7,100,49,51,507],[8,50,49,1,508],[9,0,49,-49,459]]. A2:A10 are ... | 2026-09-08T09:26:21.727903+00:00 | Chart | 2,407 | 1 | 5 | 5 | 2 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const ... | 02ed8e4baf167b0df60ed41c0995ae57c8f6bdc0e381344ba6e26b4a2b80395e | workbooks/Backward_EnronFuse.104-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | eed3163c24ffdcfd03c4c9cc8f8af02bd27fbea6031b0e184e3151bae5ab4151 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1044-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1044-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', create a chart below the existing worksheet that analyzes Monthly Spending from February through July 2001. Plot the dates on the x-axis and the values from B8:G8 on the y-axis as one marker series named 'Monthly Spending'. Title it 'Monthly Spending: Acceleration and 3-Month Trend'. Add a t... | The monthly dates are in B1:G1 and Monthly Spending values are in B8:G8. A date-based scatter chart can isolate that noncontiguous series, while adding a cubic forecast trendline and a three-month moving average requires two specifically configured additions to the series’ trendline collection. | A chart named 'MonthlySpendingTrendAnalysis' is created on 'Shortfall Calculation' at A18:I34, titled 'Monthly Spending: Acceleration and 3-Month Trend'. It contains exactly one series named 'Monthly Spending', using B1:G1 as x-values and B8:G8 as y-values. That series has exactly two trendlines: 'Cubic acceleration fi... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("MonthlySpendingTrendAnalysis");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
... | null | outputs/train/af91777da9b4f8cccd77d2e7b82b2882ec0b367f0b0c013e882300b569b1a26a/gold.xlsx | A chart named 'MonthlySpendingTrendAnalysis' is created on 'Shortfall Calculation' at A18:I34, titled 'Monthly Spending: Acceleration and 3-Month Trend'. It contains exactly one series named 'Monthly Spending', using B1:G1 as x-values and B8:G8 as y-values. That series has exactly two trendlines: 'Cubic acceleration fi... | 2026-09-08T09:26:24.261849+00:00 | Chart | 2,662 | 1 | 5 | 4 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n\n for (const existing of charts.items) {\n if (existing.name === \"MonthlySpendingAccele... | af91777da9b4f8cccd77d2e7b82b2882ec0b367f0b0c013e882300b569b1a26a | workbooks/Backward_EnronFuse.1044-File.xlsx | 37b9187afb56fa77e46fd64dbae68081b3e4f9056a2365dd6f134aae036a53bf | 189a6ffe551c3aec98abe1fe5021e174e1a0b9491ba873e0f05c7c73972d6ea1 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1060-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1060-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing the 1st Lien amount of $475,000 with the 2nd Lien amount of $200,000. Title it “Lien Balance Comparison,” hide the legend, and place the chart from F3 to M18. | Sheet1 contains two directly comparable lien balances: 1st Lien at A8/C8 ($475,000) and 2nd Lien at A9/C9 ($200,000). Creating a labeled column chart makes their relative sizes immediately visible and requires adding a new chart to the worksheet. | Sheet1 contains a new clustered column chart named "LienComparison" positioned at F3:M18, titled "Lien Balance Comparison", with no legend and two categories: 1st Lien = 475000 and 2nd Lien = 200000. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const existing = sheet.charts.getItemOrNullObject("LienComparison");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.delete();
await context.sync();
}
con... | null | outputs/train/129cdc90169cabba6d76a46a3c12519622eea3651b564ab2d23eb92933c65980/gold.xlsx | Sheet1 contains a new clustered column chart named "LienComparison" positioned at F3:M18, titled "Lien Balance Comparison", with no legend and two categories: 1st Lien = 475000 and 2nd Lien = 200000.
| 2026-09-08T09:27:31.030926+00:00 | Chart | 3,577 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const chart = sheet.charts.add(\n Excel.ChartType.columnClustered,\n sheet.getRange(\"A8:C9\"),\n Excel.ChartSeriesBy.columns\n );\n\n chart.name = \"LienBalanceComparison\";\n chart.... | 129cdc90169cabba6d76a46a3c12519622eea3651b564ab2d23eb92933c65980 | workbooks/Backward_EnronFuse.1060-File.xlsx | 1d56e342105cb7ca94ca1179de6c4cf48d68bcd74449ff766d608fc6b622d1ae | ba20d485c60fcd1a53e0c6e26433eda875722f1f919c9e1419ef043eed733b20 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1041-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1041-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', build a linked helper table in K1:N7 with the headers Month, MW Need, Avg Market Price, and MoM Price Change. Keep February through July 2001 in chronological order, use formulas to link the first three columns to the existing dates and values, leave February's change blank with a formula, a... | The monthly dates, MW Need, and market prices are stored in separate rows, while month-over-month change is absent. Building a formula-linked helper table preserves source formulas and chronology; explicitly assigning both chart series prevents automatic inclusion of the calculated change column. A synchronization and ... | 'Shortfall Calculation'!K1:N7 contains headers [Month, MW Need, Avg Market Price, MoM Price Change]. K2:K7 contains formulas =B1 through =G1; L2:L7 contains =B3 through =G3; M2:M7 contains =B4 through =G4; N2 contains ="" and N3:N7 contains =M3/M2-1, =M4/M3-1, =M5/M4-1, =M6/M5-1, and =M7/M6-1. K2:K7 is formatted mmm-yy... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const chartName = "NeedVsMarketPriceChart";
// Remove a prior chart with the required internal name.
const oldChart = sheet.charts.getItemOrNullObject(chartName);
oldChart.load("isNullObject");
... | null | outputs/train/1e444193a3d7b60927985198d2fcaf852eabb8326ddd0bbbff5ab9713ca1528a/gold.xlsx | 'Shortfall Calculation'!K1:N7 contains headers [Month, MW Need, Avg Market Price, MoM Price Change]. K2:K7 contains formulas =B1 through =G1; L2:L7 contains =B3 through =G3; M2:M7 contains =B4 through =G4; N2 contains ="" and N3:N7 contains =M3/M2-1, =M4/M3-1, =M5/M4-1, =M6/M5-1, and =M7/M6-1. K2:K7 is formatted mmm-yy... | 2026-09-08T09:27:35.482935+00:00 | Chart | 2,506 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Build the linked helper table.\n sheet.getRange(\"K1:N1\").values = [[\"Month\", \"MW Need\", \"Avg Market Price\", \"MoM Price Change\"]];\n sheet.getRange(\"K2:K7\").formu... | 1e444193a3d7b60927985198d2fcaf852eabb8326ddd0bbbff5ab9713ca1528a | workbooks/Backward_EnronFuse.1041-File.xlsx | c786f14a1f5284cc4f896ac41090b7fa05af094762623c5cd2c3a8986b7a778b | 86ff9245d5579bc278c3aede872744b512dc244311068cbbf2120bcdf66008f7 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1054-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1054-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Shortfall Calculation' sheet, create a combo chart for every 2001 month shown in the date headers. Find the data from the row labels rather than assuming fixed source cells: show 'Avg Mkt Price ($/Mwh)' and 'Shortfall ($/Mwh)' as marker lines on the primary axis, and 'Monthly Spending' as clustered columns on t... | The revised code is valid JavaScript with all TypeScript-only assertions and annotations removed. It discovers the labeled data rows and 2001 month columns from the used range, then adds three noncontiguous series with distinct chart types, axis groups, colors, and axis formats. | Shortfall Calculation!I2:P16 contains a combo chart named 'California_Cost_Combo' titled 'California Power Costs: Feb-Jul 2001'. Its date categories are Feb-01 through Jul-01. On the primary axis, blue marker-line series 'Avg Mkt Price ($/Mwh)' = [150, 157.5, 165.375, 173.64375, 182.3259375, 191.44223437500003] and red... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const used = sheet.getUsedRange();
used.load(["values", "rowCount", "columnCount"]);
const oldChart = sheet.charts.getItemOrNullObject("California_Cost_Combo");
oldChart.load("isNullObject");
aw... | null | outputs/train/28bf27e5745890ff3a4ddf32a2e79082674bf350eff1d181e548b15abdd62e15/gold.xlsx | Shortfall Calculation!I2:P16 contains a combo chart named 'California_Cost_Combo' titled 'California Power Costs: Feb-Jul 2001'. Its date categories are Feb-01 through Jul-01. On the primary axis, blue marker-line series 'Avg Mkt Price ($/Mwh)' = [150, 157.5, 165.375, 173.64375, 182.3259375, 191.44223437500003] and red... | 2026-09-08T09:27:37.999551+00:00 | Chart | 3,222 | 0 | 5 | 3 | 1 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
... | 28bf27e5745890ff3a4ddf32a2e79082674bf350eff1d181e548b15abdd62e15 | workbooks/Backward_EnronFuse.1054-File.xlsx | e7e7cf4807477f325eaebd4872238f756379551a6bd94037159298238268657f | 7bf95cfc33c27e58ef915cc9281d944cee8a8605a9ea04bee037c2d694b83667 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1060-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1060-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing the 1st Lien and 2nd Lien amounts in rows 16–17, add a linear trendline, title it “Lien Amount Comparison,” and place it in E3:L18. | Sheet1 contains two comparable lien amounts in C16:C17, labeled 1st Lien and 2nd Lien in A16:A17. A new clustered column chart can visualize those values, and the chart series’ trendline collection can be used to add the requested linear trendline. | Sheet1 contains a new clustered column chart named "LienAmountComparison" positioned at E3:L18, titled "Lien Amount Comparison", with categories "1st Lien" and "2nd Lien", values 47292 and 23160, and one linear trendline. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("LienAmountComparison");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.delete();
a... | null | outputs/train/164c0fc00273d34ec018af14d0585e2e9bd7d8652e04ef0b6fbd50899f77ea66/gold.xlsx | Sheet1 contains a new clustered column chart named "LienAmountComparison" positioned at E3:L18, titled "Lien Amount Comparison", with categories "1st Lien" and "2nd Lien", values 47292 and 23160, and one linear trendline.
| 2026-09-08T09:28:05.087232+00:00 | Chart | 3,580 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const charts = sheet.charts;\n\n // Build the chart from the labels in column A and lien amounts in column C.\n const chart = charts.add(\n Excel.ChartType.columnClustered,\n sheet.getRan... | 164c0fc00273d34ec018af14d0585e2e9bd7d8652e04ef0b6fbd50899f77ea66 | workbooks/Backward_EnronFuse.1060-File.xlsx | 1d56e342105cb7ca94ca1179de6c4cf48d68bcd74449ff766d608fc6b622d1ae | e34d054dd6101332047831b5aede225992e43a5352725ba39012ff03a63a6067 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1056-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1056-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', create a combo chart named 'California MW Need Chart' using the February–July 2001 dates in B1:G1. Show only two series: 'MW Need in CA' from B3:G3 as blue clustered columns on the primary axis, and 'Avg Mkt Price ($/Mwh)' from B4:G4 as an orange line with circle markers on the secondary axi... | The sheet has a shared monthly date row in B1:G1 and two differently scaled measures in B3:G3 and B4:G4. A combo chart requires two explicitly configured series, primary and secondary axes, and point-level formatting; July is the maximum MW Need value at 15,560. | 'Shortfall Calculation' contains exactly one replacement chart named 'California MW Need Chart' at I2:P18 with style 10 and title 'California Demand and Market Price'. It has exactly two series: primary-axis blue clustered columns named 'MW Need in CA' with category formula ='Shortfall Calculation'!$B$1:$G$1 and value ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const charts = sheet.charts;
// Replace only the chart with the requested name, preserving unrelated charts.
const existing = charts.getItemOrNullObject("California MW Need Chart");
existing.load(... | null | outputs/train/8051008cf028574f9d936ad3056608dd10db8a989314a9e0c7b11fc561b14ea3/gold.xlsx | 'Shortfall Calculation' contains exactly one replacement chart named 'California MW Need Chart' at I2:P18 with style 10 and title 'California Demand and Market Price'. It has exactly two series: primary-axis blue clustered columns named 'MW Need in CA' with category formula ='Shortfall Calculation'!$B$1:$G$1 and value ... | 2026-09-08T09:28:38.318806+00:00 | Chart | 3,322 | 1 | 5 | 2 | 1 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const chart = sheet.charts.getItem(\"California MW Need Chart\");\n chart.series.load(\"items\... | 8051008cf028574f9d936ad3056608dd10db8a989314a9e0c7b11fc561b14ea3 | workbooks/Backward_EnronFuse.1056-File.xlsx | e7e7cf4807477f325eaebd4872238f756379551a6bd94037159298238268657f | 4c4e0e829796bb15c73cc3e73a9414285489d723820357fa1503fbcf04d30246 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1055-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1055-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Shortfall Calculation' sheet, fill B10:F10 with cumulative Rolling Spending formulas based on Monthly Spending in row 8, but do not change G10; confirm that G10 remains the existing six-month total. Then create a two-series line chart with markers using the dates in B1:G1 as categories and the Monthly Spending ... | B10:F10 must be calculated cumulatively from Monthly Spending while G10 is preserved and validated. The chart then requires two explicitly defined series from noncontiguous rows, with each series receiving its value range through the supported formula property and its shared dates through categoryFormula. | Shortfall Calculation!B10:F10 contains formulas =SUM($B$8:B8), =SUM($B$8:C8), =SUM($B$8:D8), =SUM($B$8:E8), and =SUM($B$8:F8), producing -963600000, -2104726875, -3477959531.25, -5046613757.8125, and -6896675045.625. Shortfall Calculation!G10 remains -9071229097.44375. A line-with-markers chart named exactly MonthlySpe... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const rollingRange = sheet.getRange("B10:F10");
const preservedTotal = sheet.getRange("G10");
const expectedTotal = -9071229097.44375;
// Validate the existing six-month total before changing B10:... | null | outputs/train/81bf3bb71a132303c3c73d196999dfdc3210b0c909c84f785df949a333011272/gold.xlsx | Shortfall Calculation!B10:F10 contains formulas =SUM($B$8:B8), =SUM($B$8:C8), =SUM($B$8:D8), =SUM($B$8:E8), and =SUM($B$8:F8), producing -963600000, -2104726875, -3477959531.25, -5046613757.8125, and -6896675045.625. Shortfall Calculation!G10 remains -9071229097.44375. A line-with-markers chart named exactly MonthlySpe... | 2026-09-08T09:29:02.511152+00:00 | Chart | 3,271 | 1 | 5 | 4 | 1 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Preserve and verify the existing six-month total in G10.\n const g10 = sheet.getRange(\"G10\");\n const monthly = sheet.getRange(\"B8:G8\");\n g10.load([\"values\", \"formu... | 81bf3bb71a132303c3c73d196999dfdc3210b0c909c84f785df949a333011272 | workbooks/Backward_EnronFuse.1055-File.xlsx | e7e7cf4807477f325eaebd4872238f756379551a6bd94037159298238268657f | 6db5477135c987adf2fd8ea869de72c52e3e8dc014ee93ee575fa1dfaef6b484 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.106-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.106-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, calculate residual cash for periods 1–9 as Marketing revenues plus Fin. energy pmts minus Debt service/fixed pmt minus O&M, treating blank amounts as zero. Put a compact chart-data table in O5:X6, then create a line-with-markers chart titled “Residual Cash by Period ($/kw-yr)” below the model. Name the chart... | The nine periods are split across nonadjacent columns C:E, G:I, and K:M. Building a compact formula-driven series requires mapping those columns, treating blank marketing amounts as zero, and calculating revenues less debt service and O&M before creating the chart. | Sheet1!O5:X6 displays [["",1,2,3,4,5,6,7,8,9],["Residual cash",101,51,1,151,101,51,51,1,-49]], and Sheet1 contains a line-with-markers chart named "ResidualCashChart", sourced from O5:X6 with series arranged by rows, titled "Residual Cash by Period ($/kw-yr)", and positioned from A24 to M40. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("ResidualCashChart");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.delete();
awa... | null | outputs/train/ab826545a78267f0b8f27d0b1f322b579c067b3add043009358ceefe5522f6d3/gold.xlsx | Sheet1!O5:X6 displays [["",1,2,3,4,5,6,7,8,9],["Residual cash",101,51,1,151,101,51,51,1,-49]], and Sheet1 contains a line-with-markers chart named "ResidualCashChart", sourced from O5:X6 with series arranged by rows, titled "Residual Cash by Period ($/kw-yr)", and positioned from A24 to M40.
| 2026-09-08T09:30:26.737921+00:00 | Chart | 3,526 | 0 | 5 | 3 | 1 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Create a compact chart-data table for periods 1–9.\n const table = sheet.getRange(\"O5:X6\");\n table.values = [\n [\"Period\", 1, 2, 3, 4, 5, 6, 7, 8, 9],\n [\"Residual Cash\", \"\"... | ab826545a78267f0b8f27d0b1f322b579c067b3add043009358ceefe5522f6d3 | workbooks/Backward_EnronFuse.106-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | 2930d1de7f75cb9568adf092cfad2c9296a77c3ff76a170c03c667bdf18d3410 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1052-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1052-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', build a helper table in A18:G20 for an indexed comparison of MW Need and Avg Mkt Price, with February 2001 equal to 100. Set A18 to “Index Series,” link B18:G18 to the dates in B1:G1, name the two rows “MW Need Index” and “Avg Mkt Price Index,” and calculate each monthly index from B3:G3 and... | Normalizing the two source rows into a formula-driven helper table prevents Excel from inferring the chart directly from A1:G4. The chart must then be assembled with explicit date X values, calculated index Y values, two named series, distinct formatting, and a different trendline configuration for each series. | Shortfall Calculation!A18:G20 contains formulas/values [["Index Series","=B1","=C1","=D1","=E1","=F1","=G1"],["MW Need Index","=B3/$B$3*100","=C3/$B$3*100","=D3/$B$3*100","=E3/$B$3*100","=F3/$B$3*100","=G3/$B$3*100"],["Avg Mkt Price Index","=B4/$B$4*100","=C4/$B$4*100","=D4/$B$4*100","=E4/$B$4*100","=F4/$B$4*100","=G4/... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const helper = sheet.getRange("A18:G20");
helper.formulas = [
["Index Series", "=B1", "=C1", "=D1", "=E1", "=F1", "=G1"],
[
"MW Need Index",
"=B3/$B$3*100",
"=C3/$B$3*100",
... | null | outputs/train/bc7b63f85d438562bd9c7b74307b375a217d650712fe9c9500901aee93ed7c1f/gold.xlsx | Shortfall Calculation!A18:G20 contains formulas/values [["Index Series","=B1","=C1","=D1","=E1","=F1","=G1"],["MW Need Index","=B3/$B$3*100","=C3/$B$3*100","=D3/$B$3*100","=E3/$B$3*100","=F3/$B$3*100","=G3/$B$3*100"],["Avg Mkt Price Index","=B4/$B$4*100","=C4/$B$4*100","=D4/$B$4*100","=E4/$B$4*100","=F4/$B$4*100","=G4/... | 2026-09-08T09:31:29.830623+00:00 | Chart | 3,121 | 1 | 5 | 3 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n\n // Build the indexed helper table.\n sheet.getRange(\"A18\").values = [[\"Index Series\"]];\n sheet.getRange(\"A19:A20\").values = [[\"MW Need Index\"], [\"Avg Mkt Price Index\... | bc7b63f85d438562bd9c7b74307b375a217d650712fe9c9500901aee93ed7c1f | workbooks/Backward_EnronFuse.1052-File.xlsx | 9d3a1fb3811ad9f6d70c25d64090c7e1126665d579c07a19159958f8f3c2612e | 099dbc07a17c1dce03d614b1ccf9e598f2035654215d33560c1a89fd367dc709 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1055-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1055-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Shortfall Calculation sheet, create a combo chart titled “California MW Need vs. Average Market Price” positioned exactly from A18 to I33. Use B1:G1 as the February–July 2001 categories. Include exactly two series: “MW Need in CA” from B3:G3 as a line with markers on the primary axis, and “Avg Mkt Price ($/Mwh)”... | The initial B3:G4 source produces two valid series that can be reconfigured in place, avoiding an invalid zero-series chart. The supported categoryFormula and formula properties explicitly bind both series to the shared dates and their respective value rows, after which only the MW series receives the trendline. | Shortfall Calculation: chart "California MW Need vs. Average Market Price" spans A18:I33 and has exactly 2 series. Series 1 is "MW Need in CA", categories ='Shortfall Calculation'!$B$1:$G$1, values ='Shortfall Calculation'!$B$3:$G$3, type lineMarkers, primary axis, with exactly 1 linear trendline displaying its equatio... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const categoryFormula = "='Shortfall Calculation'!$B$1:$G$1";
const mwValuesFormula = "='Shortfall Calculation'!$B$3:$G$3";
const priceValuesFormula = "='Shortfall Calculation'!$B$4:$G$4";
// B3:G... | null | outputs/train/a719ad4bc592c9b498f82a1167eed19f34467edc62cb497278620e2a5feae908/gold.xlsx | Shortfall Calculation: chart "California MW Need vs. Average Market Price" spans A18:I33 and has exactly 2 series. Series 1 is "MW Need in CA", categories ='Shortfall Calculation'!$B$1:$G$1, values ='Shortfall Calculation'!$B$3:$G$3, type lineMarkers, primary axis, with exactly 1 linear trendline displaying its equatio... | 2026-09-08T09:32:27.619119+00:00 | Chart | 3,274 | 2 | 5 | 4 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const charts = sheet.charts;\n\n // Start clean so there are no unintended series or leftover partial charts.\n charts.load(\"items\");\n await context.sync();\n for (const old... | a719ad4bc592c9b498f82a1167eed19f34467edc62cb497278620e2a5feae908 | workbooks/Backward_EnronFuse.1055-File.xlsx | e7e7cf4807477f325eaebd4872238f756379551a6bd94037159298238268657f | a0a1a97f6dd42e19c32fb713a34054424e0dc2795a492969c5fd6fcc3095d44a | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1056-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1056-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', build a bubble chart below the model that shows how market stress changed from February–April to May–July 2001. Plot MW Need on the horizontal axis, Avg Mkt Price on the vertical axis, and use the magnitude of Monthly Spending for bubble size. Make the first three months one series and the l... | A bubble chart can encode three existing measures simultaneously: MW Need as x, Avg Mkt Price as y, and the absolute Monthly Spending as bubble size. Splitting the six months into early and late periods requires adding two separately configured series from hidden staging data, making the task substantially harder than ... | A new bubble chart named 'CaliforniaMarketStressBubble' on 'Shortfall Calculation', positioned at A18:I34 and titled 'California Market Stress: Early vs Late 2001', with its legend at the bottom. Series 'Feb-Apr 2001' has x-values [8800, 9925, 11375], y-values [150, 157.5, 165.375], and bubble sizes [963600000, 1141126... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Shortfall Calculation");
const oldChart = sheet.charts.getItemOrNullObject("CaliforniaMarketStressBubble");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart.delete();
}
... | null | outputs/train/c1e817fcb54f33ec064e4bf99556e5c71cafc410b339082fa7f71524b9b4ec6a/gold.xlsx | A new bubble chart named 'CaliforniaMarketStressBubble' on 'Shortfall Calculation', positioned at A18:I34 and titled 'California Market Stress: Early vs Late 2001', with its legend at the bottom. Series 'Feb-Apr 2001' has x-values [8800, 9925, 11375], y-values [150, 157.5, 165.375], and bubble sizes [963600000, 1141126... | 2026-09-08T09:33:54.790709+00:00 | Chart | 3,324 | 1 | 5 | 3 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Shortfall Calculation\");\n const charts = sheet.charts;\n\n const oldChart = charts.getItemOrNullObject(\"CaliforniaMarketStressEarlyVsLate2001\");\n oldChart.load(\"isNullObject\");\n await context.sync(... | c1e817fcb54f33ec064e4bf99556e5c71cafc410b339082fa7f71524b9b4ec6a | workbooks/Backward_EnronFuse.1056-File.xlsx | e7e7cf4807477f325eaebd4872238f756379551a6bd94037159298238268657f | 1ea5721f4c8d5feb76d0d8446fc8a007cce4e612e8608dc3e550ae497d502e03 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1074-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1074-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a helper table in O1:R9 with headers in this exact order: Period, Nymex, SoCal Outright, EPSJ Outright. Fill Period from A2:A9 and Nymex from B2:B9. For every period, calculate SoCal Outright as Nymex plus the populated SoCal basis in column I—not the empty SoCal column C—and calculate EPSJ Outright a... | The populated SoCal basis is in I2:I9 rather than the empty SoCal column C, while EPSJ basis is in L2:L9. Building O1:R9 with formulas disambiguates those fields and derives two outright-price series before creating the multi-series chart. | Sheet1!O1:R9 contains headers [Period, Nymex, SoCal Outright, EPSJ Outright] and rows [Feb-Mar, 9.35, 9.35, 8.55], [Q2, 6, 6.65, 5.44], [Q3, 5.75, 7.25, 5.2], [Q4, 4.3, 5.11, 3.9], [Cal 2, 4.6, 5.55, 4.27], [Cal 3, 3.92, 4.57, 3.66], [Cal 4, 3.8, 4.2, 3.58], [Cal 5, 3.8, 4.2, 3.58]. P2:P9 uses formulas =B2 through =B9,... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const existingChart = sheet.charts.getItemOrNullObject("OutrightPriceComparison");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
existingChart.delete();
}
... | null | outputs/train/eb087203926d79531c62f37fc316bf931147424a4a92708ea5e07756bd4adeff/gold.xlsx | Sheet1!O1:R9 contains headers [Period, Nymex, SoCal Outright, EPSJ Outright] and rows [Feb-Mar, 9.35, 9.35, 8.55], [Q2, 6, 6.65, 5.44], [Q3, 5.75, 7.25, 5.2], [Q4, 4.3, 5.11, 3.9], [Cal 2, 4.6, 5.55, 4.27], [Cal 3, 3.92, 4.57, 3.66], [Cal 4, 3.8, 4.2, 3.58], [Cal 5, 3.8, 4.2, 3.58]. P2:P9 uses formulas =B2 through =B9,... | 2026-09-08T09:34:04.528814+00:00 | Chart | 4,342 | 2 | 5 | 2 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Create the helper table in exactly O1:R9.\n const helper = sheet.getRange(\"O1:R9\");\n helper.clear(Excel.ClearApplyTo.all);\n sheet.getRange(\"O1:R1\").values = [[\"Period\", \"Nymex\",... | eb087203926d79531c62f37fc316bf931147424a4a92708ea5e07756bd4adeff | workbooks/Backward_EnronFuse.1074-File.xlsx | a69a2d48b9ae7826459de41e8970bc701fac4f80a8bfbba877fad4ae94262723 | f16cf928dea88393e72606712b5c81a2a453eb44e79bfd433a549ef5368cc6e6 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1066-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1066-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, identify the populated columns in the right-hand adjustment block that have region names, ignoring any populated but unlabeled columns. In hidden helper columns O:P, create a linked period column and a “Regional Average” that averages those named regions for each row from Feb-Mar through Cal 5. Then create a... | The right-hand adjustment block has three populated columns with region headers—SoCal in I, EPSJ in L, and Permian in M—while column K is populated but unlabeled. The task requires deriving a linked row-level average in hidden helper columns, charting that hidden data alongside nonadjacent source ranges, and applying p... | Sheet1!O1:P9 contains headers "Period" and "Regional Average"; O2:O9 contains formulas =A2 through =A9; P2:P9 contains formulas =AVERAGE(I2,L2,M2) through =AVERAGE(I9,L9,M9), yielding -0.3833333333, -0.0166666667, 0.3, 0.12, 0.2, 0.1266666667, 0.06, 0.06; and columns O:P are hidden. Sheet1 contains a line-with-markers ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Create linked helper data.
sheet.getRange("O1:P9").values = [
["Period", "Regional Average"],
[null, null], [null, null], [null, null], [null, null],
[null, null], [null, null], [null, null], [null, n... | null | outputs/train/6106f280824aab161d6ba80ee400b966a6b19147787d0723b1f187707f1406c3/gold.xlsx | Sheet1!O1:P9 contains headers "Period" and "Regional Average"; O2:O9 contains formulas =A2 through =A9; P2:P9 contains formulas =AVERAGE(I2,L2,M2) through =AVERAGE(I9,L9,M9), yielding -0.3833333333, -0.0166666667, 0.3, 0.12, 0.2, 0.1266666667, 0.06, 0.06; and columns O:P are hidden. Sheet1 contains a line-with-markers ... | 2026-09-08T09:34:15.980070+00:00 | Chart | 3,883 | 1 | 5 | 4 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Identify only populated, labeled columns in the right-hand adjustment block.\n const block = sheet.getRange(\"I1:M9\");\n block.load([\"values\", \"columnIndex\"]);\n await context.sync()... | 6106f280824aab161d6ba80ee400b966a6b19147787d0723b1f187707f1406c3 | workbooks/Backward_EnronFuse.1066-File.xlsx | 7a26117d53dc36e91d9abe48b1c047d449c022bfb3132df1bba09375428bd0d7 | d8c284a2ced4632b172f30ad1e613695fc61a84a2d02300d2f608b1c0e25fb5e | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1052-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1052-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Shortfall Calculation', create a decision-ready combo chart for April through July 2001 without altering A1:I16. Show monthly funding needed in $ billions as clustered columns, the three-month rolling funding need in $ billions as a line with markers on the same axis, and the absolute Shortfall ($/Mwh) as a line wi... | Rows 6 and 8 contain negative shortfall and spending figures, but a decision-ready chart should show positive funding requirements. Restricting the view to April–July also permits a complete three-month rolling total for every plotted month. This requires calculated helper data, three deliberately ordered series, and m... | A hidden '_ChartData' sheet with A1:D5 containing headers [Month, Monthly funding need ($bn), 3-month funding need ($bn), Shortfall ($/MWh)] and April–July values [[Apr-2001, 1.37323265625, 3.47795953125, 165.375], [May-2001, 1.5686542265625002, 4.0830137578125, 173.64375], [Jun-2001, 1.8500612878125002, 4.791948170625... | await Excel.run(async (context) => {
const source = context.workbook.worksheets.getItem("Shortfall Calculation");
const chartName = "CaliforniaFundingRequirement";
const helperName = "_ChartData";
// Remove prior benchmark output in dependency order.
const oldChart = source.charts.getItemOrNullObject(chartNa... | null | outputs/train/072c8d7340ac28d13f8e8cef8c2ae50213f4d03f3bf40cbc03eb018ee48c2dfb/gold.xlsx | A hidden '_ChartData' sheet with A1:D5 containing headers [Month, Monthly funding need ($bn), 3-month funding need ($bn), Shortfall ($/MWh)] and April–July values [[Apr-2001, 1.37323265625, 3.47795953125, 165.375], [May-2001, 1.5686542265625002, 4.0830137578125, 173.64375], [Jun-2001, 1.8500612878125002, 4.791948170625... | 2026-09-08T09:35:18.315800+00:00 | Chart | 3,120 | 2 | 5 | 5 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const workbook = context.workbook;\n const sourceSheet = workbook.worksheets.getItem(\"Shortfall Calculation\");\n const oldChart = sourceSheet.charts.getItemOrNullObject(\"CaliforniaFundingRequirement\");\n const helperCheck = workbook.worksheets.getItemOrN... | 072c8d7340ac28d13f8e8cef8c2ae50213f4d03f3bf40cbc03eb018ee48c2dfb | workbooks/Backward_EnronFuse.1052-File.xlsx | 9d3a1fb3811ad9f6d70c25d64090c7e1126665d579c07a19159958f8f3c2612e | d46642b284befc7b6aef8e1abb11eb396e92a535cb8b5a2895d0a2b34016120d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1063-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1063-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, build a linked source table in N1:O5 with headers “Funding Source” and “Funding Amount.” Use formulas to pull the labels and amounts for 1st Lien from A8/C8, 2nd Lien from A9/C9, and Cash Required from A11/C11, in that order. Put “Total” in N5 and a formula in O5 that sums the three amounts; confirm it equal... | The three funding components are noncontiguous in the original model but reconcile to the $750,000 Sales Price. A formula-linked source table keeps them dynamic, while constructing the doughnut directly from N1:O4 reliably creates exactly one three-point series and excludes the Total row. The resulting series can then ... | Sheet1!N1:O5 is a table named FundingSourceTable containing headers Funding Source and Funding Amount; rows 2–4 are formula-linked to A8/C8, A9/C9, and A11/C11 and display 1st Lien = 475000, 2nd Lien = 200000, and Cash Required = 75000; N5 is Total and O5 contains =SUM(O2:O4), returning 750000, equal to C5. Columns N:O... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Build a formula-linked source and a separate reconciliation row.
const sourceRange = sheet.getRange("N1:O5");
sourceRange.values = [
["Funding Source", "Funding Amount"],
["", ""],
["", ""],
[""... | null | outputs/train/50aa4b3aff5bd7de7599f53befdaaa2d2da70dc3119dd2f18030c8f43be0bb9c/gold.xlsx | Sheet1!N1:O5 is a table named FundingSourceTable containing headers Funding Source and Funding Amount; rows 2–4 are formula-linked to A8/C8, A9/C9, and A11/C11 and display 1st Lien = 475000, 2nd Lien = 200000, and Cash Required = 75000; N5 is Total and O5 contains =SUM(O2:O4), returning 750000, equal to C5. Columns N:O... | 2026-09-08T09:35:45.830500+00:00 | Chart | 3,732 | 0 | 5 | 4 | 2 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build the linked chart source table.\n const source = sheet.getRange(\"N1:O5\");\n source.clear(Excel.ClearApplyTo.all);\n sheet.getRange(\"N1:O1\").values = [[\"Funding Source\", \"Fundi... | 50aa4b3aff5bd7de7599f53befdaaa2d2da70dc3119dd2f18030c8f43be0bb9c | workbooks/Backward_EnronFuse.1063-File.xlsx | 1e7c009b5546a00a6faf62ba50d146d41264a9e5122d63e54d28cc04b2018491 | 1e71df7e66bd3efee5a0abbd24265738ff93fb27d51080f124b2179914272ddd | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1068-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1068-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Build a regression-comparison XY scatter chart on Sheet1 using only the Q2 through Cal 5 rows (rows 3:9), excluding Feb-Mar. Use Nymex in B3:B9 as the shared x-values and the populated basis columns for the y-values—the populated SoCal column is I, not the blank SoCal column C. Name the chart “Basis_Model_Comparison” a... | The task now requires inspecting the row labels to exclude Feb-Mar, resolving the duplicate SoCal headers by selecting the populated I column, constructing three ordered XY series from separate ranges, and adding four specifically configured trendlines—including two competing regression models on SoCal—while enforcing ... | Sheet1 contains a new XY scatter chart named "Basis_Model_Comparison" positioned from O2 through Z24 and titled "Basis Regression Comparison (Q2–Cal 5)". It has exactly three series in order: (1) "SoCal Basis", using B3:B9 and I3:I9, with blue circle markers of size 8; (2) "EPSJ Basis", using B3:B9 and L3:L9, with oran... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const chart = sheet.charts.add(
Excel.ChartType.xyscatter,
sheet.getRange("B2:B9"),
Excel.ChartSeriesBy.columns
);
chart.name = "Basis_Model_Comparison";
// Remove the placeholder series created fro... | null | outputs/train/eec3b98b3e207974ee6e20efa9334509502e2f60f2f00a496ef96eac133ce48b/gold.xlsx | Sheet1 contains a new XY scatter chart named "Basis_Model_Comparison" positioned from O2 through Z24 and titled "Basis Regression Comparison (Q2–Cal 5)". It has exactly three series in order: (1) "SoCal Basis", using B3:B9 and I3:I9, with blue circle markers of size 8; (2) "EPSJ Basis", using B3:B9 and L3:L9, with oran... | 2026-09-08T09:35:53.825436+00:00 | Chart | 3,988 | 1 | 5 | 3 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const old = sheet.charts.getItemOrNullObject(\"Basis_Model_Comparison\");\n old.load(\"isNullObject\");\n await context.sync();\n if (!old.isNullObject) { old.delete(); await context.sync(); }... | eec3b98b3e207974ee6e20efa9334509502e2f60f2f00a496ef96eac133ce48b | workbooks/Backward_EnronFuse.1068-File.xlsx | c2a61eb616b5edfe9d8455493a76c046d1290fe799284f517db291eb6decd874 | a5d16cfa7fb31e7ce683bebdd3fb761909745187f12140a4fac7015547589cda | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1070-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1070-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a linked chart-data block in O1:S9 for Term, Nymex, SoCal, EPSJ, and Permian using the existing rows 2 through 9. From that block, create a combo chart named “Outright and Basis Curves”: show Nymex as clustered columns on the primary axis, show the three basis markets as lines with markers on the seco... | Sheet1 has one outright Nymex curve in B2:B9 and three basis curves in the nonadjacent columns I, L, and M. A realistic comparison requires assembling linked data and creating a combination chart with separate axes because the outright and basis values use different scales. | Sheet1!O1:S9 contains [["Term","Nymex","SoCal","EPSJ","Permian"],["Feb-Mar",9.35,0,-0.8,-0.35],["Q2",6,0.65,-0.56,-0.14],["Q3",5.75,1.5,-0.55,-0.05],["Q4",4.3,0.81,-0.4,-0.05],["Cal 2",4.6,0.95,-0.33,-0.02],["Cal 3",3.92,0.65,-0.26,-0.01],["Cal 4",3.8,0.4,-0.22,0],["Cal 5",3.8,0.4,-0.22,0]], with O2:S9 linked by formul... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const chartData = sheet.getRange("O1:S9");
chartData.formulas = [
["Term", "Nymex", "SoCal", "EPSJ", "Permian"],
["=A2", "=B2", "=I2", "=L2", "=M2"],
["=A3", "=B3", "=I3", "=L3", "=M3"],
["=A4", "=B4... | null | outputs/train/2ae76803cab26e19b5d9111c2f163f012e5253300e9c7abc940b2265c9d19f3a/gold.xlsx | Sheet1!O1:S9 contains [["Term","Nymex","SoCal","EPSJ","Permian"],["Feb-Mar",9.35,0,-0.8,-0.35],["Q2",6,0.65,-0.56,-0.14],["Q3",5.75,1.5,-0.55,-0.05],["Q4",4.3,0.81,-0.4,-0.05],["Cal 2",4.6,0.95,-0.33,-0.02],["Cal 3",3.92,0.65,-0.26,-0.01],["Cal 4",3.8,0.4,-0.22,0],["Cal 5",3.8,0.4,-0.22,0]], with O2:S9 linked by formul... | 2026-09-08T09:36:42.527887+00:00 | Chart | 4,138 | 1 | 5 | 4 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const block = sheet.getRange(\"O1:S9\");\n block.clear(Excel.ClearApplyTo.all);\n sheet.getRange(\"O1:S1\").values = [[\"Term\", \"Nymex\", \"SoCal\", \"EPSJ\", \"Permian\"]];\n sheet.getRan... | 2ae76803cab26e19b5d9111c2f163f012e5253300e9c7abc940b2265c9d19f3a | workbooks/Backward_EnronFuse.1070-File.xlsx | 70771629917bc0b46b2740689b0034691416be9cf686d0fbdba1f0ca87300593 | 272370d2beb689d47f0a11dffbd9b7766d3b9db2f0d4b4ef77831b44bece8194 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1061-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1061-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, find and match the “1st Lien” and “2nd Lien” rows in the financing section with the same labels in the annual debt-service section; do not rely on hard-coded source cell addresses. Replace any existing table named `LienDebtServiceHelper` with a linked helper table in the unused area P3:S5 containing Lien Typ... | The runtime does not reliably support formatting individual scatter points through `series.points`. This revision removes the distinct per-point color requirement, retains a single two-point series so the linear trendline covers both liens, and uses only series-level marker formatting. The source rows are still discove... | Sheet1!P3:S5 is a table named LienDebtServiceHelper with headers [Lien Type, Balance, Annual Debt Service, Debt-Service Percentage]; P4:S4 displays [1st Lien, $475,000, $47,292, 9.96%] and P5:S5 displays [2nd Lien, $200,000, $23,160, 11.58%]. Sheet1 contains exactly one chart named LienBalanceVsDebtServiceChart over F3... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const usedRange = sheet.getUsedRange();
usedRange.load(["values", "rowIndex", "columnIndex"]);
const charts = sheet.charts;
charts.load("items/name");
const oldTable = context.workbook.tables.getItemOrNullObj... | null | outputs/train/7d77dfb9a81f78022914c9cec4137013adab5af8f340a467bd3b4e9483d4a67a/gold.xlsx | Sheet1!P3:S5 is a table named LienDebtServiceHelper with headers [Lien Type, Balance, Annual Debt Service, Debt-Service Percentage]; P4:S4 displays [1st Lien, $475,000, $47,292, 9.96%] and P5:S5 displays [2nd Lien, $200,000, $23,160, 11.58%]. Sheet1 contains exactly one chart named LienBalanceVsDebtServiceChart over F3... | 2026-09-08T09:36:57.397138+00:00 | Chart | 3,631 | 1 | 5 | 4 | 1 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const existingTable = sheet.tables.getItemOrNullObject(\"LienDebtServiceHelper\");\n const existingChart = ... | 7d77dfb9a81f78022914c9cec4137013adab5af8f340a467bd3b4e9483d4a67a | workbooks/Backward_EnronFuse.1061-File.xlsx | 3444615a71dffe161b3cfcb20376a98c5f5278bd600bd67b9428e5f7f1754539 | 7d508b228a40fad9aabda3346036018845806b982ce9924445c38cae4151b9be | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.108-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.108-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a line chart on Sheet1 comparing Marketing revenues, Fin. energy pmts, and Debt service/fixed pmt across periods 1 through 9. Title it “Revenue and Payment Profile” and place it below the existing data. | Sheet1 has nine periods split across columns C:E, G:I, and K:M and three comparable rows for marketing revenue, energy payments, and debt service. The chart is first created from a contiguous helper range, then its series are recreated one at a time with a sync after each addition so indices 0, 1, and 2 are valid when ... | Sheet1 contains a line-with-markers chart named CashFlowComponentsChart at A24:M40 titled “Revenue and Payment Profile”. Its categories are periods 1–9, with series Marketing revenues = [100, 50, blank, 100, 50, blank, 100, 50, blank], Fin. energy pmts = [50, 50, 50, 100, 100, 100, 0, 0, 0], and Debt service/fixed pmt ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const oldChart = sheet.charts.getItemOrNullObject("CashFlowComponentsChart");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart.delete();
await context.sync();
... | null | outputs/train/d4ad71e1187df8a5cc58c192bc5bf616e92d8d22669a4475c2c218e15da9f6ac/gold.xlsx | Sheet1 contains a line-with-markers chart named CashFlowComponentsChart at A24:M40 titled “Revenue and Payment Profile”. Its categories are periods 1–9, with series Marketing revenues = [100, 50, blank, 100, 50, blank, 100, 50, blank], Fin. energy pmts = [50, 50, 50, 100, 100, 100, 0, 0, 0], and Debt service/fixed pmt ... | 2026-09-08T09:37:38.816108+00:00 | Chart | 4,650 | 0 | 5 | 2 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Remove a prior copy if this task is run again.\n const oldChart = sheet.charts.getItemOrNullObject(\"RevenuePaymentProfile\");\n oldChart.load(\"isNullObject\");\n await context.sync();\n... | d4ad71e1187df8a5cc58c192bc5bf616e92d8d22669a4475c2c218e15da9f6ac | workbooks/Backward_EnronFuse.108-File.xlsx | bb15777d4b212871792d084734e1093dc2f200c6dd55ee282ec32cfbe91ad37b | 7ae5e9fb995e99588557f6d40e519af60fd2529a21b02d9d3bdbf9148b740bbe | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1076-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1076-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a formula-driven helper table in A12:D20 with the headers Period, Nymex, SoCal Outright, and Permian Outright for Feb-Mar through Cal 5. Link Period and Nymex to the original data, and calculate each regional outright price as Nymex plus the corresponding populated basis from the SoCal and Permian col... | The populated regional basis data are in I2:I9 for SoCal and M2:M9 for Permian, not under the blank duplicate headers in columns C and G. The task first transforms those basis values into outright prices with formulas in a helper table, then deliberately constructs and orders the chart series from that derived table us... | Sheet1!A12:D20 has headers [Period, Nymex, SoCal Outright, Permian Outright]. Rows 13:20 have formulas [=$A2, =$B2, =$B2+$I2, =$B2+$M2] through [=$A9, =$B9, =$B9+$I9, =$B9+$M9], producing values [[Feb-Mar, 9.35, 9.35, 9.00], [Q2, 6.00, 6.65, 5.86], [Q3, 5.75, 7.25, 5.70], [Q4, 4.30, 5.11, 4.25], [Cal 2, 4.60, 5.55, 4.5... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Build the derived, formula-driven source table.
sheet.getRange("A12:D12").values = [[
"Period",
"Nymex",
"SoCal Outright",
"Permian Outright"
]];
sheet.getRange("A13:D20").formulas = [
["... | null | outputs/train/6c483d2327abb2486deb25aa456b4dbefe1c07f7573178b143d61660688447d2/gold.xlsx | Sheet1!A12:D20 has headers [Period, Nymex, SoCal Outright, Permian Outright]. Rows 13:20 have formulas [=$A2, =$B2, =$B2+$I2, =$B2+$M2] through [=$A9, =$B9, =$B9+$I9, =$B9+$M9], producing values [[Feb-Mar, 9.35, 9.35, 9.00], [Q2, 6.00, 6.65, 5.86], [Q3, 5.75, 7.25, 5.70], [Q4, 4.30, 5.11, 4.25], [Cal 2, 4.60, 5.55, 4.5... | 2026-09-08T09:38:14.260567+00:00 | Chart | 4,446 | 0 | 5 | 2 | 1 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n sheet.getRange(\"A12:D12\").values = [[\"Period\", \"Nymex\", \"SoCal Outright\", \"Permian Outright\"]];\n sheet.getRange(\"A13:A20\").formulas = [\n [\"=A2\"], [\"=A3\"], [\"=A4\"], [\"=A... | 6c483d2327abb2486deb25aa456b4dbefe1c07f7573178b143d61660688447d2 | workbooks/Backward_EnronFuse.1076-File.xlsx | ca2c781ee494db3616db9a97fb9b99bff80421cfae1cba81252da28f08a176c4 | 89277f4d7f39e2b312efe0692e59018aa946b1ba824d278e9987a40cf7352910 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1082-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1082-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a line-with-markers chart on Sheet1 comparing the SoCal and PG&E basis values across the periods in A2:A9, using I2:I9 for SoCal and J2:J9 for PG&E. Title it “SoCal vs PG&E Basis Outlook”. Add a linear trendline named “SoCal linear fit” only to SoCal, and a 3-period moving-average trendline named “PG&E 3-period ... | The basis data are in nonadjacent columns from their period labels: periods are A2:A9, while SoCal and PG&E are I2:I9 and J2:J9. Building a comparison chart therefore requires explicitly using the period labels for both series, then adding a different trendline type to each series through its trendline collection. | Sheet1 contains a new line-with-markers chart named "BasisOutlookChart" at A11:J27, titled "SoCal vs PG&E Basis Outlook", with the legend hidden. Its SoCal series uses A2:A9 as categories and I2:I9 as values and has exactly one linear trendline named "SoCal linear fit". Its PG&E series uses A2:A9 as categories and J2:J... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Remove only a prior benchmark-created chart so the result is repeatable.
const existing = sheet.charts.getItemOrNullObject("BasisOutlookChart");
existing.load("isNullObject");
await context.sync();
if (!exi... | null | outputs/train/6160474bca311c1c87e81f2ce7f18087fd70ce3b2e31b5e50999d55a1f9a355a/gold.xlsx | BasisOutlookChart | SoCal trendlines: 1 | PG&E trendlines: 1
| 2026-09-08T09:40:32.644142+00:00 | Chart | 4,804 | 2 | 5 | 2 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const oldChart = sheet.charts.getItemOrNullObject(\"SoCal_vs_PGE_Basis_Outlook\");\n oldChart.load(\"isNullObject\");\n await context.sync();\n if (!oldChart.isNullObject) {\n oldChart.de... | 6160474bca311c1c87e81f2ce7f18087fd70ce3b2e31b5e50999d55a1f9a355a | workbooks/Backward_EnronFuse.1082-File.xlsx | 821b03ed1b0d836a4950840606ea303f4e16f8bc515393424de3cbdcd1ec9d50 | 71cda62e99f2084541148e95298379a4f1b9c537e2eb4ba4f61a251233ce8bd1 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.109-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.109-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a line chart named “RevenuePaymentsComparison” at O3:W18 comparing Marketing revenues and Fin. energy pmts for periods 1 and 2. Use the period numbers as the horizontal-axis labels, title it “Revenue and Financial Energy Payments,” and show the legend on the right. | Sheet1 contains period labels 1 and 2 in C5:D5, Marketing revenues in C7:D7, and Fin. energy pmts in C8:D8. A line chart can be created from the marketing data, with the financial-energy-payment data added as a new chart series. | Sheet1 contains a line chart named RevenuePaymentsComparison at O3:W18 titled “Revenue and Financial Energy Payments”, with categories [1, 2], Marketing revenues values [100, 50], Fin. energy pmts values [50, 50], and the legend on the right. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
const existingChart = charts.getItemOrNullObject("RevenuePaymentsComparison");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
exi... | null | outputs/train/887d40f404dcd31892e4d261a48a990f73256e211f77dc3184e9b5e761a9ae81/gold.xlsx | Sheet1 contains a line chart named RevenuePaymentsComparison at O3:W18 titled “Revenue and Financial Energy Payments”, with categories [1, 2], Marketing revenues values [100, 50], Fin. energy pmts values [50, 50], and the legend on the right.
| 2026-09-08T09:40:59.433445+00:00 | Chart | 5,211 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const existing = sheet.charts.getItemOrNullObject(\"RevenuePaymentsComparison\");\n existing.load(\"isNullObject\");\n await context.sync();\n if (!existing.isNullObject) {\n existing.delet... | 887d40f404dcd31892e4d261a48a990f73256e211f77dc3184e9b5e761a9ae81 | workbooks/Backward_EnronFuse.109-File.xlsx | 69df2023d13a307de82f98ca1f56a5889e7fa91f35e4d38ef1540de36eb82a52 | 9e5c540674a2be29bc4dec432e4efdb5f63b0b83056e82e085ab4486b7eee155 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1078-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1078-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Build a combo chart on Sheet1 for Feb-Mar through Cal 5 that shows SoCal, PG&E, EPSJ, and Permian basis as clustered columns on the primary axis and Nymex as a line with markers on the secondary axis. Ignore all blank columns and the unlabeled values in column K. Title it “Nymex and Regional Basis Outlook,” label the l... | Sheet1 mixes an absolute Nymex price series in B2:B9 with four regional basis series in I:J and L:M, while intervening columns are blank or unlabeled. A useful comparison requires a custom combination chart that retains only those five nonadjacent series and separates their different scales onto primary and secondary a... | Sheet1 has a new combo chart named "Nymex and Regional Basis Outlook Chart" at A11:M30 with categories Feb-Mar, Q2, Q3, Q4, Cal 2, Cal 3, Cal 4, and Cal 5. SoCal, PG&E, EPSJ, and Permian are clustered-column series on the primary axis scaled from -1 to 2; Nymex is a line-with-markers series on the secondary axis scaled... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Include the full block initially so the chart can derive the real headers and periods.
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sheet.getRange("A1:M9"),
Excel.ChartSeriesBy.col... | null | outputs/train/4fc6b91a14b4e75c85b616cb97a20e1309f56442bc622216d632f5b54a27872f/gold.xlsx | Sheet1 has a new combo chart named "Nymex and Regional Basis Outlook Chart" at A11:M30 with categories Feb-Mar, Q2, Q3, Q4, Cal 2, Cal 3, Cal 4, Cal 5. SoCal, PG&E, EPSJ, and Permian are clustered-column series on the primary axis scaled from -1 to 2; Nymex is a line-with-markers series on the secondary axis scaled fro... | 2026-09-08T09:41:57.317481+00:00 | Chart | 4,546 | 2 | 5 | 3 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const charts = sheet.charts;\n\n const existing = charts.getItemOrNullObject(\"NymexRegionalBasisOutlook\");\n existing.load(\"isNullObject\");\n await context.sync();\n if (!existing.isNullO... | 4fc6b91a14b4e75c85b616cb97a20e1309f56442bc622216d632f5b54a27872f | workbooks/Backward_EnronFuse.1078-File.xlsx | ca2c781ee494db3616db9a97fb9b99bff80421cfae1cba81252da28f08a176c4 | d39938268550c97068c44b811665c8a6639467b65c4f5217584bd7c95441d8ae | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1075-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1075-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, build a regression comparison chart for the contract segments, excluding Feb-Mar. Create an XY scatter chart named `NymexSoCalTrend`, positioned exactly over O2:Y22, with exactly two series in this order: “Quarterly” from Q2–Q4 (rows 3:5) and “Calendar” from Cal 2–Cal 5 (rows 6:9). In both series, use Nymex ... | The labels in A3:A9 divide the relevant observations into quarterly and calendar contracts, while B and I contain paired Nymex and SoCal Basis values. Requiring separate series, different trendline types, displayed regression statistics, fixed axis scales, precise formatting, and strict series/trendline counts makes th... | Sheet1 contains an XY scatter chart named NymexSoCalTrend exactly over O2:Y22, titled “Quarterly vs Calendar Regression,” with a bottom legend, horizontal axis “Nymex” scaled 3.5–6.5 by 0.5, and vertical axis “SoCal Basis” scaled 0–1.6 by 0.2. It has exactly two series in order: Quarterly, with blue (#4472C4) size-9 ci... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Initialize a valid scatter chart with one reusable series.
const chart = sheet.charts.add(
Excel.ChartType.xyscatter,
sheet.getRange("B1:B3"),
Excel.ChartSeriesBy.columns
);
chart.name = "NymexSoC... | null | outputs/train/c0b3f86497839ed1811d4a37b50dafb902a9fbcd1d1c522df7409763d4f67315/gold.xlsx | Sheet1 contains an XY scatter chart named NymexSoCalTrend exactly over O2:Y22, titled “Quarterly vs Calendar Regression,” with a bottom legend, horizontal axis “Nymex” scaled 3.5–6.5 by 0.5, and vertical axis “SoCal Basis” scaled 0–1.6 by 0.2. It has exactly two series in order: Quarterly, with blue (#4472C4) size-9 ci... | 2026-09-08T09:45:14.163267+00:00 | Chart | 4,396 | 1 | 5 | 6 | 2 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const charts = sheet.charts;\n const old = charts.getItemOrNullObject(\"NymexSoCalTrend\");\n old.load(\"isNullObject\");\n await context.sync();\n if (!old.isNullObject) { old.delete(); awai... | c0b3f86497839ed1811d4a37b50dafb902a9fbcd1d1c522df7409763d4f67315 | workbooks/Backward_EnronFuse.1075-File.xlsx | a69a2d48b9ae7826459de41e8970bc701fac4f80a8bfbba877fad4ae94262723 | ab530fdc01e8c37b456e832a075bbfb1fbf3f4d66d7cf95781381e0739fec535 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1091-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1091-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a line chart on Sheet1 titled “Nymex vs. Implied Delivered Prices” over O2:X18. Use the periods in A2:A9 on the horizontal axis and show five lines in this order: Nymex, SoCal, PG&E, EPSJ, and Permian. For each regional line, calculate the implied delivered price as that period’s Nymex value in column B plus the... | The revised solution remains challenging because it derives four delivered-price series from noncontiguous basis columns, preserves a missing value as a chart gap, creates a hidden helper dataset, and adds five chart series in a specified order. All TypeScript annotations have been removed so the snippet is valid JavaS... | Sheet1 contains a line chart named "ImpliedDeliveredPricesChart" at O2:X18 titled "Nymex vs. Implied Delivered Prices", with series ordered Nymex, SoCal, PG&E, EPSJ, Permian and periods A2:A9 as categories. A very hidden sheet named "_ChartData" contains A1:F9 = [["Period","Nymex","SoCal","PG&E","EPSJ","Permian"],["Feb... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const source = sheet.getRange("A2:M9");
source.load("values");
await context.sync();
const values = source.values;
const round2 = (value) => Math.round((value + Number.EPSILON) * 100) / 100;
const isMissing ... | null | outputs/train/8d1b10360d4bf2468319715c419fb32ec25ef440f9159ce89571d01655d7b7a8/gold.xlsx | Sheet1 contains a line chart named "ImpliedDeliveredPricesChart" at O2:X18 titled "Nymex vs. Implied Delivered Prices", with series ordered Nymex, SoCal, PG&E, EPSJ, Permian and periods A2:A9 as categories. A very hidden sheet named "_ChartData" contains A1:F9 = [["Period","Nymex","SoCal","PG&E","EPSJ","Permian"],["Feb... | 2026-09-08T09:45:34.241035+00:00 | Chart | 5,313 | 1 | 5 | 3 | 1 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Read the periods, Nymex prices, and the four requested regional basis columns.\n const source = sheet.getRange(\"A2:M9\");\n source.load(\"values\");\n await context.sync();\n\n const v ... | 8d1b10360d4bf2468319715c419fb32ec25ef440f9159ce89571d01655d7b7a8 | workbooks/Backward_EnronFuse.1091-File.xlsx | fdc0efe4d2187fa77913142be19f4bd42508795569da1f7942c7afdab8543127 | aa9a67c6ab861d1bc0b17e0fd5c7b49eb1b2356f82a286a237fc64169bee67db | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.110-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.110-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a line chart with markers below the data on Sheet1 comparing Marketing revenues with Fin. energy pmts for periods 1 through 9. Treat blank Marketing revenues entries as zero, title it “Marketing Revenues vs Financial Energy Payments,” and place it from A20 to H35. | The source values for periods 1–9 are split across three column blocks. A helper table provides contiguous range-backed chart data, while adding the second series through its SERIES formula exercises series insertion without unsupported direct array assignments. | Sheet1!O3:X5 contains [["",1,2,3,4,5,6,7,8,9],["Marketing revenues",100,50,0,100,50,0,100,50,0],["Fin. energy pmts",50,50,50,100,100,100,0,0,0]], and Sheet1 contains a line-markers chart named RevenueVsPayments at A20:H35 titled “Marketing Revenues vs Financial Energy Payments,” with those nine categories and two serie... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const oldChart = sheet.charts.getItemOrNullObject("RevenueVsPayments");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart.delete();
await context.sync();
}
... | null | outputs/train/247b15b54614f5ce1d299c4a8b47131d2c1b4ed5ac87b45d976cc78cfa64072f/gold.xlsx | Sheet1!O3:X5 contains [["",1,2,3,4,5,6,7,8,9],["Marketing revenues",100,50,0,100,50,0,100,50,0],["Fin. energy pmts",50,50,50,100,100,100,0,0,0]], and Sheet1 contains a line-markers chart named RevenueVsPayments at A20:H35 titled “Marketing Revenues vs Financial Energy Payments,” with those nine categories and two serie... | 2026-09-08T09:46:05.028623+00:00 | Chart | 5,823 | 0 | 5 | 3 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Read periods 1-9 and the two source rows from their actual, noncontiguous columns.\n const source = sheet.getRange(\"C5:M8\");\n source.load(\"values\");\n const charts = sheet.charts;\n ... | 247b15b54614f5ce1d299c4a8b47131d2c1b4ed5ac87b45d976cc78cfa64072f | workbooks/Backward_EnronFuse.110-File.xlsx | 728021313966aa7cc6496ac865643aee7dda1965a6af1c0e9692eddd94748fd4 | d9eb43c147eba1dc83c947869da87b4a96dee8eb7c3d1d47a023fe92564f1db9 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1084-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1084-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, build a formula-driven helper table in A12:E20 showing each regional basis change versus that region’s Q2 basis for every tenor from Feb-Mar through Cal 5. Use the source headers and values from the populated regional basis block so the table includes only SoCal, PG&E, EPSJ, and Permian in that order; do not... | The requested chart must be based on derived values rather than the original basis columns. A12:E20 can remain source-linked with formulas referencing tenors in A2:A9, region names in I1/J1/L1/M1, and each region’s Q2 value in row 3. Charting this contiguous helper table preserves series order and responsiveness while ... | Sheet1!A12:E20 is a formula-linked helper table with headers [Tenor, SoCal, PG&E, EPSJ, Permian] and rows: [Feb-Mar, -0.65, blank, -0.24, -0.21], [Q2, 0, 0, 0, 0], [Q3, 0.85, 0.55, 0.01, 0.09], [Q4, 0.16, 0.39, 0.16, 0.09], [Cal 2, 0.30, 0.38, 0.23, 0.12], [Cal 3, 0, 0.08, 0.30, 0.13], [Cal 4, -0.25, -0.17, 0.34, 0.14]... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Build a source-linked helper table. The empty-string branch preserves
// missing observations instead of converting them to zero.
sheet.getRange("A12:E20").formulas = [
["Tenor", "=I1", "=J1", "=L1", "=M1"]... | null | outputs/train/316166e2b808b1caba6629ad71541b7dfbc729e05f980f29557a105dc5cafee1/gold.xlsx | Sheet1!A12:E20 is a formula-linked helper table with headers [Tenor, SoCal, PG&E, EPSJ, Permian] and rows: [Feb-Mar, -0.65, blank, -0.24, -0.21], [Q2, 0, 0, 0, 0], [Q3, 0.85, 0.55, 0.01, 0.09], [Q4, 0.16, 0.39, 0.16, 0.09], [Cal 2, 0.30, 0.38, 0.23, 0.12], [Cal 3, 0, 0.08, 0.30, 0.13], [Cal 4, -0.25, -0.17, 0.34, 0.14]... | 2026-09-08T09:46:34.917533+00:00 | Chart | 4,903 | 1 | 5 | 5 | 2 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const helper = sheet.getRange(\"A12:E20\");\n helper.clear(Excel.ClearApplyTo.all);\n sheet.getRange(\"A12... | 316166e2b808b1caba6629ad71541b7dfbc729e05f980f29557a105dc5cafee1 | workbooks/Backward_EnronFuse.1084-File.xlsx | 821b03ed1b0d836a4950840606ea303f4e16f8bc515393424de3cbdcd1ec9d50 | e75efeab2127e9af8a9da86861912fa92643a769acda0efd0022701403c1e579 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1093-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1093-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a dual-axis combo chart on Sheet1 comparing the market data by contract period. Plot Nymex from column B as a line with markers on the secondary axis, and plot SoCal, PG&E, Malin, EPSJ, and Permian from columns I:M as clustered columns on the primary axis; treat the unlabeled column K as Malin. Title it “Nymex P... | The workbook contains Nymex prices in B2:B9 and regional basis values in I2:M9. A dual-axis combo chart is appropriate because Nymex and basis differentials use different scales, while a helper range supplies the missing Malin header and combines the nonadjacent source ranges. The code uses plain JavaScript syntax for ... | Sheet1 contains a combo chart named “NymexVsRegionalBasis” at O2:W18, titled “Nymex Price vs. Regional Basis”. Its categories are Feb-Mar, Q2, Q3, Q4, Cal 2, Cal 3, Cal 4, and Cal 5; Nymex is a line-with-markers series on the secondary axis; SoCal, PG&E, Malin, EPSJ, and Permian are clustered-column series on the prima... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const periods = sheet.getRange("A2:A9");
const nymex = sheet.getRange("B2:B9");
const basis = sheet.getRange("I2:M9");
periods.load("values");
nymex.load("values");
basis.load("values");
const oldChart = ... | null | outputs/train/6a90c55fd925ea9cd9fce65724fcc94e07d33796a56bbf91ee5180d7da98e34a/gold.xlsx | Sheet1 contains a combo chart named “NymexVsRegionalBasis” at O2:W18, titled “Nymex Price vs. Regional Basis”. Its categories are Feb-Mar, Q2, Q3, Q4, Cal 2, Cal 3, Cal 4, and Cal 5; Nymex is a line-with-markers series on the secondary axis; SoCal, PG&E, Malin, EPSJ, and Permian are clustered-column series on the prima... | 2026-09-08T09:46:52.196249+00:00 | Chart | 5,413 | 1 | 5 | 3 | 1 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n sheet.getRange(\"K1\").values = [[\"Malin\"]];\n\n const charts = sheet.charts;\n charts.load(\"items\");\n await context.sync();\n for (const existing of charts.items) existing.delete();\n\n... | 6a90c55fd925ea9cd9fce65724fcc94e07d33796a56bbf91ee5180d7da98e34a | workbooks/Backward_EnronFuse.1093-File.xlsx | fdc0efe4d2187fa77913142be19f4bd42508795569da1f7942c7afdab8543127 | ad4a261ad9939f7a9bd9c98e408e0373798d65c2cb9eb557c6998abaf6cf0b26 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1089-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1089-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a line-with-markers chart titled “Nymex and Regional Basis Outlook” using the delivery periods in column A. Select the basis series by inspecting the right-hand outlook block: include every column there that has both a label and at least one populated outlook value, but ignore the duplicate empty head... | The corrected plain-JavaScript solution explicitly loads the worksheet name before reading it. It then inspects the source data to select only populated, labeled outlook columns, creates direct noncontiguous series references in the required order, configures and verifies the chart, and confirms the source values and f... | Sheet1 contains a chart named “NymexRegionalBasisOutlook” positioned at A11:M29 and titled “Nymex and Regional Basis Outlook”. Its categories directly reference Sheet1!A2:A9. Its series, in legend order, directly reference SoCal I2:I9, PG&E J2:J9, EPSJ L2:L9, Permian M2:M9, and Nymex B2:B9; K is excluded, and no helper... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const source = sheet.getRange("A1:M9");
sheet.load("name");
source.load(["values", "formulas"]);
const existing = sheet.charts.getItemOrNullObject("NymexRegionalBasisOutlook");
existing.load("isNullObject");
... | null | outputs/train/792309bde3b11191d7c5a1d8cd29a588e805b6ce6fc477a7ef6386c7aef0d483/gold.xlsx | Sheet1 contains a chart named “NymexRegionalBasisOutlook” positioned at A11:M29 and titled “Nymex and Regional Basis Outlook”. Its categories directly reference Sheet1!A2:A9. Its series, in legend order, directly reference SoCal I2:I9, PG&E J2:J9, EPSJ L2:L9, Permian M2:M9, and Nymex B2:B9; K is excluded, and no helper... | 2026-09-08T09:48:14.940883+00:00 | Chart | 5,158 | 1 | 5 | 5 | 2 | 0.2 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const ... | 792309bde3b11191d7c5a1d8cd29a588e805b6ce6fc477a7ef6386c7aef0d483 | workbooks/Backward_EnronFuse.1089-File.xlsx | fdc0efe4d2187fa77913142be19f4bd42508795569da1f7942c7afdab8543127 | c62b13cf9f0bea1ff8df8d6c064e2ec575361b16c1bcbbb258dffb93c7cafad0 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1087-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1087-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, set N1 to exactly “SoCal-Permian spread” and set N2:N9 to the row-specific formulas =ROUND(I2-M2,2) through =ROUND(I9-M9,2). Delete all existing charts on Sheet1, then create one XY scatter chart so Sheet1 ends with exactly one chart. Give its chart object the exact name “chtBasisSpreadDiagnostics”, give it ... | The solution now deletes every pre-existing chart so the worksheet ends with exactly one chart. It assigns all spread formulas explicitly, creates and removes the chart’s automatic series, then adds the required series and trendlines. All count requests are queued before the final synchronization, so the newly added se... | Sheet1!N1:N9 contains [“SoCal-Permian spread”, 0.35, 0.79, 1.55, 0.86, 0.97, 0.66, 0.4, 0.4], with N2:N9 containing =ROUND(I2-M2,2) through =ROUND(I9-M9,2). Sheet1 has exactly one chart: an XY scatter chart named “chtBasisSpreadDiagnostics”, positioned O2:W20 with style 10 and title “Nymex Basis and Spread Diagnostics”... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Prepare the derived spread data with supported explicit formulas.
sheet.getRange("N1").values = [["SoCal-Permian spread"]];
sheet.getRange("N2:N9").formulas = [
["=ROUND(I2-M2,2)"],
["=ROUND(I3-M3,2)"],... | null | outputs/train/0e30069eb2625d277cce9afbd700137640cc3119fe3b324aac1efa83bfcf1641/gold.xlsx | Sheet1!N1:N9 contains [“SoCal-Permian spread”, 0.35, 0.79, 1.55, 0.86, 0.97, 0.66, 0.4, 0.4], with N2:N9 containing =ROUND(I2-M2,2) through =ROUND(I9-M9,2). Sheet1 has exactly one chart: an XY scatter chart named “chtBasisSpreadDiagnostics”, positioned O2:W20 with style 10 and title “Nymex Basis and Spread Diagnostics”... | 2026-09-08T09:48:25.791518+00:00 | Chart | 5,059 | 2 | 5 | 5 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n sheet.getRange(\"N1\").values = [[\"SoCal-Permian spread\"]];\n sheet.getRange(\"N2:N9\").formulas = [\n [\"=ROUND(I2-M2,2)\"], [\"=ROUND(I3-M3,2)\"],\n [\"=ROUND(I4-M4,2)\"], [\"=ROUND(... | 0e30069eb2625d277cce9afbd700137640cc3119fe3b324aac1efa83bfcf1641 | workbooks/Backward_EnronFuse.1087-File.xlsx | fdc0efe4d2187fa77913142be19f4bd42508795569da1f7942c7afdab8543127 | 61dede122b56b3f8c44379b49d21d49ad2e825435936ded7ecaf2528eddccecd | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1111-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1111-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing Summer, Winter Average, and Winter Peak supply for Sumas Receipts and Roosevelt North. Title it “Supply by Season and Winter Scenario,” place the legend at the bottom, and position the chart from H2 to N16. | Sheet1 contains two supply locations in A4:A5 and three comparable figures in columns C, E, and F, with headers Summer, Average, and Peak. Creating a clustered column chart from A2:F5 uses the existing labels and values while preserving the Winter Average/Peak grouping implied by the merged Winter heading. | Sheet1 contains a new clustered column chart named SupplySeasonComparison, sourced from Sheet1!A2:F5, titled “Supply by Season and Winter Scenario,” with the legend at the bottom and positioned from H2 to N16. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sheet.getRange("A2:F5"),
Excel.ChartSeriesBy.columns
);
chart.name = "SupplySeasonComparison";
chart.title.text = "Supply by Season an... | null | outputs/train/7973e8a4c0f2fc21e120924fd8a81d678fb5d8f83179056d8a335c5d3f1cd3fb/gold.xlsx | Sheet1 contains a new clustered column chart named SupplySeasonComparison, sourced from Sheet1!A2:F5, titled “Supply by Season and Winter Scenario,” with the legend at the bottom and positioned from H2 to N16.
| 2026-09-08T09:48:56.658686+00:00 | Chart | 6,280 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build a compact, contiguous source range from the requested supply data.\n const source = sheet.getRange(\"P1:S3\");\n source.values = [\n [\"Supply\", \"Summer\", \"Winter Average\", \... | 7973e8a4c0f2fc21e120924fd8a81d678fb5d8f83179056d8a335c5d3f1cd3fb | workbooks/Backward_EnronFuse.1111-File.xlsx | 7826b6f02fbee790f0b359fc57e41510c8f61d7c937cec40a115f976a4b10344 | 8c8674b6c8b40a21beb896261d32756402952064b9c2871254bde4b93aa7b074 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.11-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.11-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, find the row labeled “Equity” in column A and include the consecutive numeric constants in column B starting on that row, stopping before any blank, text, error, or formula cell so later summaries are excluded. In unused D:E, create a helper table headed “Period” and “Equity Value”; number the observations s... | The revised solution is plain JavaScript and discovers the Equity block by finding its label and stopping at the first nonnumeric or formula cell in column B. It then builds explicit period/value helper ranges, regression formulas, and a repeatable named XY chart with a linear trendline. It also validates the known 36-... | Sheet1!D1:E37 contains headers [Period, Equity Value] followed by periods 1–36 and the discovered Equity constants from B2:B37. Sheet1!D40:E42 contains [Slope, =SLOPE(E2:E37,D2:D37)], [Intercept, =INTERCEPT(E2:E37,D2:D37)], and [R-squared, =RSQ(E2:E37,D2:D37)], with E40:E42 formatted as 0.0000. Sheet1 has a chart named... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const used = sheet.getUsedRange();
used.load(["values", "formulas", "rowIndex", "columnIndex", "rowCount", "columnCount"]);
await context.sync();
const values = used.values;
const formulas = used.formulas;
c... | null | outputs/train/273a3d19fc4281384f68bd074724691d379838524b07da8db792e8a9c38bf74d/gold.xlsx | Sheet1!D1:E37 contains headers [Period, Equity Value] followed by periods 1–36 and the discovered Equity constants from B2:B37. Sheet1!D40:E42 contains [Slope, =SLOPE(E2:E37,D2:D37)], [Intercept, =INTERCEPT(E2:E37,D2:D37)], and [R-squared, =RSQ(E2:E37,D2:D37)], with E40:E42 formatted as 0.0000. Sheet1 has a chart named... | 2026-09-08T09:49:07.804866+00:00 | Chart | 5,773 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const source = sheet.getRange(\"A1:B45\");\n source.load([\"values\", \"formulas\", \"valueTypes\", \"rowCount\"]);\n await context.sync();\n\n let equityRow = -1;\n for (let r = 0; r < sourc... | 273a3d19fc4281384f68bd074724691d379838524b07da8db792e8a9c38bf74d | workbooks/Backward_EnronFuse.11-File.xlsx | ae8bea30de0c0490436d46c4be2466591d325251b6749f6dbd428aa9c511e99c | 5efce5469cee84d1060099cf7a8fe4f5c6841630fbde88d97a4d9348f9e41292 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1095-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1095-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, fill C1:G9 as a regional outright-price table with headers SoCal, PG&E, Malin, EPSJ, and Permian. For each period, use formulas to add Nymex in column B to the matching basis value in columns I:M, but return a blank when the basis cell is blank so Feb-Mar PG&E stays missing. Create a line-with-markers chart ... | The blank C:G area must first be populated with formulas combining Nymex in B with the matching basis columns I:M, including conditional blank handling for PG&E in D2. The chart then requires reordered series references, nonadjacent category labels, gap handling, and precise axis and placement settings. The revised sni... | Sheet1!C1:G1 = [SoCal, PG&E, Malin, EPSJ, Permian]. For every row r from 2 through 9, C[r] contains =IF(I[r]="","",$B[r]+I[r]), D[r] contains =IF(J[r]="","",$B[r]+J[r]), E[r] contains =IF(K[r]="","",$B[r]+K[r]), F[r] contains =IF(L[r]="","",$B[r]+L[r]), and G[r] contains =IF(M[r]="","",$B[r]+M[r]); consequently D2 disp... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
sheet.getRange("C1:G1").values = [[
"SoCal",
"PG&E",
"Malin",
"EPSJ",
"Permian"
]];
const formulas = [];
for (let row = 2; row <= 9; row++) {
formulas.push([
`=IF(I${row}="","",$B$... | null | outputs/train/2002f0a3d6fa8c716c82d777d67fc1e43dff86e0975e78eadadce90fca0f39d1/gold.xlsx | Sheet1!C1:G1 = [SoCal, PG&E, Malin, EPSJ, Permian]. For every row r from 2 through 9, C[r] contains =IF(I[r]="","",$B[r]+I[r]), D[r] contains =IF(J[r]="","",$B[r]+J[r]), E[r] contains =IF(K[r]="","",$B[r]+K[r]), F[r] contains =IF(L[r]="","",$B[r]+L[r]), and G[r] contains =IF(M[r]="","",$B[r]+M[r]); consequently D2 disp... | 2026-09-08T09:49:28.559001+00:00 | Chart | 5,515 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n sheet.getRange(\"C1:G1\").values = [[\"SoCal\", \"PG&E\", \"Malin\", \"EPSJ\", \"Permian\"]];\n const formulas = [];\n for (let r = 2; r <= 9; r++) {\n formulas.push([\"I\", \"J\", \"K\", ... | 2002f0a3d6fa8c716c82d777d67fc1e43dff86e0975e78eadadce90fca0f39d1 | workbooks/Backward_EnronFuse.1095-File.xlsx | 93ef33702cfbb98cc3b066e7a1e83fa932a2806839761dd02a3300b1e4fb46eb | dfdf85f1fee714ed441c07d512aa8079275698bd6b7eab88c522190c93b73770 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1085-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1085-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a bubble chart named “BasisDisagreementMap” titled “Regional Basis Disagreement Map” using Nymex prices in B2:B9 on the horizontal axis and the basis values for SoCal, PG&E, EPSJ, and Permian from columns I, J, L, and M on the vertical axis. For each contract period, size each regional bubble as 5 plu... | A bubble chart requires each added series to have coordinated x-values, y-values, and bubble sizes. Here the regional data is nonadjacent, PG&E has a missing observation, and each bubble size must be derived row-by-row from the mean of the available regional values, making explicit series construction necessary. | Sheet1 contains a bubble chart named BasisDisagreementMap at O1:Y21 titled “Regional Basis Disagreement Map”. Its horizontal axis is “Nymex Price” bounded 3.5–9.5, its vertical axis is “Basis Differential” bounded -1–2, and its right-side legend identifies four series whose points are listed as (x, y, bubble size): SoC... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const input = sheet.getRange("B2:M9");
input.load("values");
await context.sync();
const definitions = [
{ name: "SoCal", index: 7, x: [], y: [], sizes: [] },
{ name: "PG&E", index: 8, x: [], y: [], size... | null | outputs/train/73124c0265e95b1b1470e83bb5fad46b1615c416b2820e89e20c46e9c9573171/gold.xlsx | Sheet1 contains a bubble chart named BasisDisagreementMap at O1:Y21 titled “Regional Basis Disagreement Map”. Its horizontal axis is “Nymex Price” bounded 3.5–9.5, its vertical axis is “Basis Differential” bounded -1–2, and its right-side legend identifies four series whose points are listed as (x, y, bubble size): SoC... | 2026-09-08T09:49:51.276376+00:00 | Chart | 4,956 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const periodsRange = sheet.getRange(\"A2:A9\");\n const nymexRange = sheet.getRange(\"B2:B9\");\n const ba... | 73124c0265e95b1b1470e83bb5fad46b1615c416b2820e89e20c46e9c9573171 | workbooks/Backward_EnronFuse.1085-File.xlsx | 821b03ed1b0d836a4950840606ea303f4e16f8bc515393424de3cbdcd1ec9d50 | 144ab3c77e70aeca43e969c10a6ff41ec16dbce0a98ac0a9a8151572dad3db5c | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1097-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1097-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, build a chart-ready helper table in A12:F20 for the terms Feb-Mar through Cal 5. Sort the regional series from highest to lowest based on their Cal 5 values, so the columns are Term, PG&E, SoCal, Malin, Permian, and EPSJ. Use formulas that reference the original cells, and preserve the blank PG&E Feb-Mar poi... | The Cal 5 values rank the regions as PG&E (0.58), SoCal (0.40), Malin (0.25), Permian (0.00), and EPSJ (-0.22). The helper table reorders the source columns accordingly with formulas, while NA() preserves the missing PG&E Feb-Mar point as an unplotted gap. The chart series are then explicitly added from that reordered ... | Sheet1!A12:F20 contains headers [Term, PG&E, SoCal, Malin, Permian, EPSJ]. A13:A20 contains formulas =A2 through =A9; B13:B20 contains =IF(J2="",NA(),J2) through =IF(J9="",NA(),J9); C13:C20 contains =IF(I2="",NA(),I2) through =IF(I9="",NA(),I9); D13:D20 contains =IF(K2="",NA(),K2) through =IF(K9="",NA(),K9); E13:E20 co... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
sheet.getRange("A12:F20").clear(Excel.ClearApplyTo.all);
sheet.getRange("A12:F12").values = [[
"Term", "PG&E", "SoCal", "Malin", "Permian", "EPSJ"
]];
const sourceColumns = ["J", "I", "K", "M", "L"];
cons... | null | outputs/train/aa82a550926ce3473e93413acbead3220366e048744a39d715c349c992267975/gold.xlsx | Sheet1!A12:F20 contains headers [Term, PG&E, SoCal, Malin, Permian, EPSJ]. A13:A20 contains formulas =A2 through =A9; B13:B20 contains =IF(J2="",NA(),J2) through =IF(J9="",NA(),J9); C13:C20 contains =IF(I2="",NA(),I2) through =IF(I9="",NA(),I9); D13:D20 contains =IF(K2="",NA(),K2) through =IF(K9="",NA(),K9); E13:E20 co... | 2026-09-08T09:49:55.837927+00:00 | Chart | 5,619 | 1 | 5 | 3 | 1 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build the helper table in Cal 5 descending order: PG&E, SoCal, Malin, Permian, EPSJ.\n const helper = sheet.getRange(\"A12:F20\");\n helper.clear(Excel.ClearApplyTo.all);\n sheet.getRange... | aa82a550926ce3473e93413acbead3220366e048744a39d715c349c992267975 | workbooks/Backward_EnronFuse.1097-File.xlsx | 93ef33702cfbb98cc3b066e7a1e83fa932a2806839761dd02a3300b1e4fb46eb | 253f5b9593a0cc1fe6e39809cdb4620376ddfff7a2bcad9551342f3d7858c67a | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1101-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1101-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, populate C2:G9 with regional outright prices by adding each row’s Nymex price in column B to the matching SoCal, PG&E, Malin, EPSJ, and Permian basis in columns I:M. Use formulas, and return a blank when the matching basis cell is blank so PG&E for Feb-Mar remains blank. Then create a line chart with circula... | The task derives five outright-price columns from Nymex plus the corresponding regional basis, preserves the missing PG&E value in J2 as a blank, and then explicitly adds the derived series in a non-source order. The revised snippet uses JavaScript syntax accepted by the execution environment. | Sheet1!C2:G9 = [[9.35, blank, 9.25, 8.55, 9], [6.65, 6.75, 6.3, 5.44, 5.86], [7.25, 7.05, 6.75, 5.2, 5.7], [5.11, 5.44, 5.06, 3.9, 4.25], [5.55, 5.73, 5.4, 4.27, 4.58], [4.57, 4.75, 4.42, 3.66, 3.91], [4.2, 4.38, 4.05, 3.58, 3.8], [4.2, 4.38, 4.05, 3.58, 3.8]], calculated with blank-preserving formulas. Sheet1 also con... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Build blank-preserving outright-price formulas in C:G.
const basisColumns = ["I", "J", "K", "L", "M"];
const formulas = [];
for (let row = 2; row <= 9; row++) {
formulas.push(
basisColumns.map((basi... | null | outputs/train/6819182ece1f9fa03ed0209ae5735866b3c984018d83d6ff3e27e217fd0db6b1/gold.xlsx | Sheet1!C2:G9 = [[9.35, blank, 9.25, 8.55, 9], [6.65, 6.75, 6.3, 5.44, 5.86], [7.25, 7.05, 6.75, 5.2, 5.7], [5.11, 5.44, 5.06, 3.9, 4.25], [5.55, 5.73, 5.4, 4.27, 4.58], [4.57, 4.75, 4.42, 3.66, 3.91], [4.2, 4.38, 4.05, 3.58, 3.8], [4.2, 4.38, 4.05, 3.58, 3.8]], calculated with blank-preserving formulas. Sheet1 also con... | 2026-09-08T09:50:41.374076+00:00 | Chart | 5,925 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const formulas = [];\n for (let r = 2; r <= 9; r++) {\n formulas.push([\n `=IF(I${r}=\"\",\"\",$B${r}+I${r})`,\n `=IF(J${r}=\"\",\"\",$B${r}+J${r})`,\n `=IF(K${r}=\"\",\"\",$... | 6819182ece1f9fa03ed0209ae5735866b3c984018d83d6ff3e27e217fd0db6b1 | workbooks/Backward_EnronFuse.1101-File.xlsx | 93ef33702cfbb98cc3b066e7a1e83fa932a2806839761dd02a3300b1e4fb46eb | 49d133d8c7b0c0a3d6b9072e2055422c0c6f399f87fd7878f4300679eeb027eb | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1099-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1099-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Leave every value and formula in Sheet1!A1:M9 unchanged. Build a formula-driven helper table in A12:G20 with these exact headers in this order: Period, Nymex, SoCal Implied, Malin Implied, EPSJ Implied, Permian Implied, Four-Hub Average. Link Period and Nymex back to columns A and B for Feb-Mar through Cal 5. Calculate... | This version preserves the original A1:M9 source area, builds a linked formula-driven helper table in A12:G20, and creates the chart from that table. Explicitly adding five series after the initial Nymex series enables exact series order and distinct per-series line and marker formatting. | Sheet1!A1:M9 is unchanged. Sheet1!A12:G20 contains headers [Period, Nymex, SoCal Implied, Malin Implied, EPSJ Implied, Permian Implied, Four-Hub Average]. Rows 13:20 contain formulas following the exact row patterns A13=A2 through A20=A9; B13=B2 through B20=B9; C13=B2+I2 through C20=B9+I9; D13=B2+K2 through D20=B9+K9; ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const source = sheet.getRange("A1:M9");
// Capture the source area so the code can verify that it remains unchanged.
source.load(["values", "formulas"]);
await context.sync();
const originalValues = JSON.strin... | null | outputs/train/3bbbd67dc833124d59196283c205a28e47354db5b5e5f8c335f2e9e0127b9648/gold.xlsx | Sheet1!A1:M9 is unchanged. Sheet1!A12:G20 contains headers [Period, Nymex, SoCal Implied, Malin Implied, EPSJ Implied, Permian Implied, Four-Hub Average]. Rows 13:20 contain formulas following the exact row patterns A13=A2 through A20=A9; B13=B2 through B20=B9; C13=B2+I2 through C20=B9+I9; D13=B2+K2 through D20=B9+K9; ... | 2026-09-08T09:50:43.513395+00:00 | Chart | 5,721 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n sheet.getRange(\"A12:G12\").values = [[\n \"Period\", \"Nymex\", \"SoCal Implied\", \"Malin Implied\",\n \"EPSJ Implied\", \"Permian Implied\", \"Four-Hub Average\"\n ]];\n const formul... | 3bbbd67dc833124d59196283c205a28e47354db5b5e5f8c335f2e9e0127b9648 | workbooks/Backward_EnronFuse.1099-File.xlsx | 93ef33702cfbb98cc3b066e7a1e83fa932a2806839761dd02a3300b1e4fb46eb | ef4b2f9ce47e7319e6b1bc491c339322c4836ecb0d17056bb2bad111ef130c92 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1112-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1112-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing the Summer supply for Sumas Receipts and Roosevelt North. Title it “Summer Supply by Source,” hide the legend, and place it in H2:N16. | Sheet1 contains Summer supply values of 800 for Sumas Receipts and 250 for Roosevelt North in rows 4–5. A clustered column chart can be created directly from that existing source range to compare the two suppliers visually. | Sheet1 contains a clustered column chart named "SummerSupplyChart" in H2:N16, titled "Summer Supply by Source", with columns for Sumas Receipts = 800 and Roosevelt North = 250, and no legend. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const existingChart = sheet.charts.getItemOrNullObject("SummerSupplyChart");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
existingChart.delete();
}
const... | null | outputs/train/b3162ae2177a83330eb3204cf37d4a69455c0474b67dde00519e5563618b43c6/gold.xlsx | Sheet1 contains a clustered column chart named "SummerSupplyChart" in H2:N16, titled "Summer Supply by Source", with columns for Sumas Receipts = 800 and Roosevelt North = 250, and no legend.
| 2026-09-08T09:50:53.507483+00:00 | Chart | 6,535 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const chart = sheet.charts.add(\n Excel.ChartType.columnClustered,\n sheet.getRange(\"A4:C5\"),\n Excel.ChartSeriesBy.columns\n );\n\n chart.title.text = \"Summer Supply by Source\";\n... | b3162ae2177a83330eb3204cf37d4a69455c0474b67dde00519e5563618b43c6 | workbooks/Backward_EnronFuse.1112-File.xlsx | e8a19ec338bd43f1dbcbb07b1dff105a0471bfb1d3f8c2b31ad6502d9b58c6a6 | 1382fdd3bb3847393895c578ad205574827013dfc06562f6e670dc638245a7d4 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1114-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1114-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing the Summer, Winter Average, and Winter Peak supply values for Sumas Receipts and Roosevelt North. Title it “Supply by Source and Season” and place it to the right of the data. | The supply data on Sheet1 lists Sumas Receipts and Roosevelt North with Summer, Winter Average, and Winter Peak values. A clustered column chart created from A2:F5 can visually compare those three measures across the two supply sources. | Sheet1 contains a clustered column chart named "SupplyComparisonChart", sourced from Sheet1!A2:F5 with series plotted by columns, titled "Supply by Source and Season", and positioned from H2 to P16. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("SupplyComparisonChart");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.delete();
}
... | null | outputs/train/7cb10d7d9cbc261e613a2e6ebcc72a7f4613bdb37928c4c2b711d488721dcd69/gold.xlsx | Sheet1 contains a clustered column chart named "SupplyComparisonChart", sourced from Sheet1!A2:F5 with series plotted by columns, titled "Supply by Source and Season", and positioned from H2 to P16.
| 2026-09-08T09:51:40.867009+00:00 | Chart | 6,637 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Remove a chart left by any prior run of this task.\n const oldChart = sheet.charts.getItemOrNullObject(\"SupplyBySourceAndSeason\");\n oldChart.load(\"isNullObject\");\n await context.syn... | 7cb10d7d9cbc261e613a2e6ebcc72a7f4613bdb37928c4c2b711d488721dcd69 | workbooks/Backward_EnronFuse.1114-File.xlsx | 900326c76fda04921ded94ebbee61703ffef06c2d167b389da94948212e56c70 | 1a4be81eb710c81136dcaf398925ac5f96b798f6b85b5a2259d5053c8cc51297 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1118-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1118-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, create a clustered column chart comparing Beg. Bal, End. Bal, and Change for transactions 35599 and 38385. Title it “Cash Balance Comparison” and place it in M2:T18. | The transaction identifiers 35599 and 38385 are in I1:J1, with their beginning balances, ending balances, and changes in rows 3, 5, and 7. A new clustered column chart can be initialized and then populated with three explicitly added series using those ranges. | A new clustered column chart named "Cash Balance Comparison" on 'Total Cash Flow' at M2:T18, titled "Cash Balance Comparison", with categories 35599 and 38385 and three series: Beg. Bal = [5000, 200], End. Bal = [11775, 35160], and Change = [6775, 34960]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
// Use a text cell only to initialize the new chart; its generated series,
// if any, will be removed before adding the requested series.
const chart = sheet.charts.add(
Excel.ChartType.columnClustere... | null | outputs/train/816573c6831622c8df0104bf4956253b16cb6bc0147b4011e419ba6c4b2a6eca/gold.xlsx | A new clustered column chart named "Cash Balance Comparison" on 'Total Cash Flow' at M2:T18, titled "Cash Balance Comparison", with categories 35599 and 38385 and three series: Beg. Bal = [5000, 200], End. Bal = [11775, 35160], and Change = [6775, 34960].
| 2026-09-08T09:52:33.467014+00:00 | Chart | 6,843 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Build a compact source table containing only the requested balances.\n const source = sheet.getRange(\"M20:O23\");\n source.values = [\n [\"Balance Type\", \"35599\", \"38385\"... | 816573c6831622c8df0104bf4956253b16cb6bc0147b4011e419ba6c4b2a6eca | workbooks/Backward_EnronFuse.1118-File.xlsx | 7ae259bee93c3f065170dbd1f9d97a2306778a4f31ee6396d514e58f844a2911 | 9c0293999d978c99536c445fd1598db7668b3115ccb7df6a7edb26cfb8a61e4f | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1116-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1116-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing Sumas Receipts and Roosevelt North across Summer, Winter Average, and Winter Peak. Title it “Supply by Season” and place it in H2:P16. | Sheet1 contains two supply sources in A4:A5 and three scenario value columns: Summer in C4:C5, Winter Average in E4:E5, and Winter Peak in F4:F5. Creating a chart and explicitly adding each scenario as a separate series exercises the series collection’s add capability. | Sheet1 contains a clustered column chart named “SupplyBySeasonChart” at H2:P16, titled “Supply by Season”, with categories [Sumas Receipts, Roosevelt North] and three series: Summer [800, 250], Winter Average [950, 200], and Winter Peak [1050, 500]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
// Create the chart, then remove its automatically generated series so the
// three requested series can be added explicitly.
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sheet.getRange... | null | outputs/train/05045f2c25db6d47b0e97c0b52860c6531cc00ba93d70a235b96f04b019e9722/gold.xlsx | Sheet1 contains a clustered column chart named “SupplyBySeasonChart” at H2:P16, titled “Supply by Season”, with categories [Sumas Receipts, Roosevelt North] and three series: Summer [800, 250], Winter Average [950, 200], and Winter Peak [1050, 500].
| 2026-09-08T09:52:38.126822+00:00 | Chart | 6,741 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Create a compact, chart-ready source table linked to the original supply data.\n const chartData = sheet.getRange(\"A18:D20\");\n chartData.values = [\n [\"\", \"Summer\", \"Winter Aver... | 05045f2c25db6d47b0e97c0b52860c6531cc00ba93d70a235b96f04b019e9722 | workbooks/Backward_EnronFuse.1116-File.xlsx | 770b13b3a45351e43bfa9c9bdf402cba7d19978a0f09477d7f44332e34897cd6 | 7e2ed98a1cf3303639209366a5dc169c300581783d206b0d9f9f402a6f9a4430 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1115-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1115-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Create a clustered column chart on Sheet1 comparing Sumas Receipts and Roosevelt North across Summer, Winter Average, and Winter Peak. Title it “Supply by Season”, show the legend at the bottom, and place it from H2 to N16. | The supply-source labels are in A4:A5, with Summer values in C4:C5 and Winter Average and Peak values in E4:F5. Creating the chart from the category range and explicitly adding the three data series exercises the series collection’s add capability. | Sheet1 contains a clustered column chart named SupplyBySeason at H2:N16, titled “Supply by Season”, with categories [Sumas Receipts, Roosevelt North] and three series: Summer [800, 250], Winter Average [950, 200], and Winter Peak [1050, 500]; the legend is at the bottom. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sheet.getRange("A4:A5"),
Excel.ChartSeriesBy.columns
);
chart.name = "SupplyBySeason";
chart.title.text = "Supply by Season";
chart.... | null | outputs/train/5d3039b9e20e040d12cbed0242696d781c34c59fbad17b322aa02c409544b158/gold.xlsx | Sheet1 contains a clustered column chart named SupplyBySeason at H2:N16, titled “Supply by Season”, with categories [Sumas Receipts, Roosevelt North] and three series: Summer [800, 250], Winter Average [950, 200], and Winter Peak [1050, 500]; the legend is at the bottom.
| 2026-09-08T09:52:45.692629+00:00 | Chart | 6,690 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n charts.items.forEach((c) => {\n if (c.name === \"SupplyBySeason\") c.delete();\n });\n\n const sour... | 5d3039b9e20e040d12cbed0242696d781c34c59fbad17b322aa02c409544b158 | workbooks/Backward_EnronFuse.1115-File.xlsx | 770b13b3a45351e43bfa9c9bdf402cba7d19978a0f09477d7f44332e34897cd6 | 1052e88974880a88d0901e2c7d6fbeb994e9cf856954e4080c4316c14481621d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1110-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1110-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create an audit table in Y1:AC6 with the headers Location, Minimum, Maximum, Spread, and Rank for the five basis locations in I1:M1. Calculate each location’s minimum and maximum from I2:M9, treat blank cells such as J2 as missing rather than zero, calculate max-minus-min spread, and rank the locations from ... | The task requires inspecting I2:M9 while excluding the blank J2 from calculations, deriving each location’s spread and rank, writing an auditable summary, explicitly constructing chart series in calculated rank order, and using the trendline collection to add exactly one linear trendline to the derived rank-1 series. | Sheet1!Y1:AC6 contains [["Location","Minimum","Maximum","Spread","Rank"],["SoCal",0,1.5,1.5,1],["Malin",-0.1,1,1.1,2],["PG&E",0.58,1.3,0.72,3],["EPSJ",-0.8,-0.22,0.58,4],["Permian",-0.35,0,0.35,5]], with Z2:AB6 formatted as 0.00. Sheet1 has a line-with-markers chart named “BasisDifferentialVariabilityChart” at O2:W18, ... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const headersRange = sheet.getRange("I1:M1");
const valuesRange = sheet.getRange("I2:M9");
headersRange.load("values");
valuesRange.load("values");
const oldChart = sheet.charts.getItemOrNullObject("BasisDiffe... | null | outputs/train/308e4a8992aa51068b044fbb3d7c16270ec35a5b14e02a7aa300cbc859e01b30/gold.xlsx | Sheet1!Y1:AC6 contains [["Location","Minimum","Maximum","Spread","Rank"],["SoCal",0,1.5,1.5,1],["Malin",-0.1,1,1.1,2],["PG&E",0.58,1.3,0.72,3],["EPSJ",-0.8,-0.22,0.58,4],["Permian",-0.35,0,0.35,5]], with Z2:AB6 formatted as 0.00. Sheet1 has a line-with-markers chart named “BasisDifferentialVariabilityChart” at O2:W18, ... | 2026-09-08T09:53:59.182911+00:00 | Chart | 6,232 | 2 | 5 | 4 | 2 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Read the five basis-location headers and their observations.\n const headersRange = sheet.getRange(\"I1:M1\");\n const dataRange = sheet.getRange(\"I2:M9\");\n headersRange.load(\"values\... | 308e4a8992aa51068b044fbb3d7c16270ec35a5b14e02a7aa300cbc859e01b30 | workbooks/Backward_EnronFuse.1110-File.xlsx | 1b325b90844318e461911d1abfd4e4aad7275da71eeaa69440a3e2aa26d3b468 | f0b0230cbc8e1f86a3ac8a0dfa7d601d10a74f8c6893c195ed44e810c8c2ffca | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1123-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1123-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart comparing the Beginning Balance, Ending Balance, and Change for 35599 and 38385. Title it 'Cash Balance Comparison', show the legend on the right, and place it in M2:T18. | The worksheet contains two cash-flow identifiers in I1:J1 (35599 and 38385) and corresponding Beginning Balance, Ending Balance, and Change values in rows 3, 5, and 7. A clustered column chart can compare these balances side by side. | A new clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow', positioned at M2:T18 and titled 'Cash Balance Comparison', using G1:J7 as its source data with series organized by columns; the chart compares 35599 (Beginning Balance 5000, Ending Balance 11775, Change 6775) and 38385 (Beginning Balance 2... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const source = sheet.getRange("G1:J7");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
source,
Excel.ChartSeriesBy.columns
);
chart.name = "CashBalanceComparison";
chart... | null | outputs/train/de00cb13b486ea63506ae0e4e9c548d27fc3dcca03f10b54360faa3809c8fc4d/gold.xlsx | A new clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow', positioned at M2:T18 and titled 'Cash Balance Comparison', using G1:J7 as its source data with series organized by columns; the chart compares 35599 (Beginning Balance 5000, Ending Balance 11775, Change 6775) and 38385 (Beginning Balance 2... | 2026-09-08T09:55:16.167053+00:00 | Chart | 7,147 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Create a compact source table for the requested comparison.\n const source = sheet.getRange(\"M20:O23\");\n source.formulas = [\n [\"\", \"=I1\", \"=J1\"],\n [\"Beginning Ba... | de00cb13b486ea63506ae0e4e9c548d27fc3dcca03f10b54360faa3809c8fc4d | workbooks/Backward_EnronFuse.1123-File.xlsx | 3a70e82b3179adfd01f026608d0b9fc0314078bc58f16c62510921f5723ad4d0 | d815406e81d19b395d0dd913dc3d70aeee5951e2faea2802f1f09a8fbf614676 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1105-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1105-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Without changing the source table, find which basis series in columns I:M has the strongest absolute Pearson correlation with Nymex in B2:B9, using only rows where both values are numeric. Create an XY scatter chart for that winning pair, add both a linear trendline and a third-order polynomial trendline, and show the ... | The task first compares the actual Nymex values in B2:B9 against all five basis series in I:M. Permian has the strongest absolute Pearson correlation (linear R² ≈ 0.942676), so the code builds a scatter chart from the nonadjacent B and M ranges and adds both linear and cubic trendlines. The solution is plain JavaScript... | Sheet1 contains an XY scatter chart named “Best Basis Fit - Permian” at O2:W18, titled “Best Basis Fit: Permian”, with one series named “Permian Basis” using B2:B9 as X values and M2:M9 as Y values. Permian is selected because its linear R-squared is 0.942676, and the series has exactly two trendlines: one linear and o... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const data = sheet.getRange("B1:M9");
data.load("values");
await context.sync();
const values = data.values;
const candidates = [
{ sheetColumn: "I", index: 7 },
{ sheetColumn: "J", index: 8 },
{ s... | null | outputs/train/d27fcdd28e242df3eb73cfcf467a943479f22160db38e1e52aa36f4937e0b51a/gold.xlsx | Sheet1 contains an XY scatter chart named “Best Basis Fit - Permian” at O2:W18, titled “Best Basis Fit: Permian”, with one series named “Permian Basis” using B2:B9 as X values and M2:M9 as Y values. Permian is selected because its linear R-squared is 0.942676, and the series has exactly 2 trendlines: one linear and one... | 2026-09-08T09:55:22.837932+00:00 | Chart | 6,334 | 0 | 5 | 4 | 2 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
... | d27fcdd28e242df3eb73cfcf467a943479f22160db38e1e52aa36f4937e0b51a | workbooks/Backward_EnronFuse.1105-File.xlsx | 1b325b90844318e461911d1abfd4e4aad7275da71eeaa69440a3e2aa26d3b468 | 35a9793d37b70c2f6f425d5f6a16d8944a2411a58e6d789267e7d0bd700c90a1 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1108-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1108-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Without changing any existing data in A:M, create a helper summary in Sheet1!O1:R9. Use the headers “Contract Period”, “Nymex”, “Highest Regional Price”, and “Region”. For rows 2:9, link Contract Period and Nymex to columns A and B, calculate the highest regional price across SoCal through Permian in C:G with MAX, and ... | The helper summary derives each period’s maximum across the five regional columns and identifies its region. The chart is created from only P1:Q9, guaranteeing exactly two value series, while O2:O9 is explicitly assigned as the category-label range. Loading the series names ensures the linear trendline is applied only ... | Sheet1!O1:R9 contains headers [Contract Period, Nymex, Highest Regional Price, Region] and rows [Feb-Mar, 9.35, 9.35, SoCal], [Q2, 6, 6.75, PG&E ], [Q3, 5.75, 7.25, SoCal], [Q4, 4.3, 5.44, PG&E ], [Cal 2, 4.6, 5.73, PG&E ], [Cal 3, 3.92, 4.75, PG&E ], [Cal 4, 3.8, 4.38, PG&E ], [Cal 5, 3.8, 4.38, PG&E ], with formula-l... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const oldChart = sheet.charts.getItemOrNullObject("NymexRegionalComparisonChart");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart.delete();
await context.syn... | null | outputs/train/2b80c10d9780a221bef0bb9934576b2bcb329c449c0d4998ced3a0f5c7e266fe/gold.xlsx | Sheet1!O1:R9 contains headers [Contract Period, Nymex, Highest Regional Price, Region] and rows [Feb-Mar, 9.35, 9.35, SoCal], [Q2, 6, 6.75, PG&E ], [Q3, 5.75, 7.25, SoCal], [Q4, 4.3, 5.44, PG&E ], [Cal 2, 4.6, 5.73, PG&E ], [Cal 3, 3.92, 4.75, PG&E ], [Cal 4, 3.8, 4.38, PG&E ], [Cal 5, 3.8, 4.38, PG&E ], with formula-l... | 2026-09-08T09:55:36.920673+00:00 | Chart | 6,487 | 2 | 5 | 4 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Build the helper summary without modifying A:M.\n sheet.getRange(\"O1:R1\").values = [[\n \"Contract Period\",\n \"Nymex\",\n \"Highest Regional Price\",\n \"Region\"\n ]];\n\n... | 2b80c10d9780a221bef0bb9934576b2bcb329c449c0d4998ced3a0f5c7e266fe | workbooks/Backward_EnronFuse.1108-File.xlsx | 1b325b90844318e461911d1abfd4e4aad7275da71eeaa69440a3e2aa26d3b468 | 14275843a65c94c5acb51cf95bd257c0a17ca4e83dfd562dbfe74a81214f7706 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1115-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1115-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create an XY scatter chart using only Sumas Receipts, Roosevelt North, and N & S Chehalis, with Winter Average as X and Winter Peak as Y. Label each point with its entry name, title the axes “Winter Average” and “Winter Peak,” set the X-axis from 0 to 1,300 and the Y-axis from 0 to 1,800, and add exactly one... | The three records occupy nonadjacent rows, so the code explicitly reads A4/A5/A9, E4/E5/E9, and F4/F5/F9 and stages only those records on a very-hidden helper sheet. The corrected snippet is valid JavaScript, adds the required linear trendline, configures labels and axes, and verifies key chart properties before printi... | Sheet1 contains a chart object named “WinterAverageVsPeak” over H2:N16 with the displayed title “Winter Average vs Peak.” It plots only Sumas Receipts (950, 1050), Roosevelt North (200, 500), and N & S Chehalis (1175, 1600); each point is labeled with its entry name. The X-axis is titled “Winter Average” with bounds 0–... | await Excel.run(async (context) => {
const workbook = context.workbook;
const sheet = workbook.worksheets.getItem("Sheet1");
const helperName = "_WinterChartData";
const expectedMessage = "Sheet1 contains a chart object named “WinterAverageVsPeak” over H2:N16 with the displayed title “Winter Average vs Peak.” I... | null | outputs/train/c3c23af24c97d93520dbad8ab1f17e7e38e0b3aa7db1e1fb96f2626a34e14369/gold.xlsx | Sheet1 contains a chart object named “WinterAverageVsPeak” over H2:N16 with the displayed title “Winter Average vs Peak.” It plots only Sumas Receipts (950, 1050), Roosevelt North (200, 500), and N & S Chehalis (1175, 1600); each point is labeled with its entry name. The X-axis is titled “Winter Average” with bounds 0–... | 2026-09-08T09:55:39.086118+00:00 | Chart | 6,691 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const workbook = context.workbook;\n const sheet = workbook.worksheets.getItem(\"Sheet1\")... | c3c23af24c97d93520dbad8ab1f17e7e38e0b3aa7db1e1fb96f2626a34e14369 | workbooks/Backward_EnronFuse.1115-File.xlsx | 770b13b3a45351e43bfa9c9bdf402cba7d19978a0f09477d7f44332e34897cd6 | 7c9f2f10038ec02ede3988cac48a604990db7bdeec8065f5d36335172c9b11be | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1119-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1119-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart titled 'Cash Balance Comparison' comparing Beg. Bal, End. Bal, and Change for 35599 and 38385. Place the chart from M2 to T18. | The source values are arranged in separate rows on 'Total Cash Flow'. A contiguous helper range can combine the two category headers with the Beg. Bal, End. Bal, and Change values, allowing Excel to generate all three chart series reliably when the new chart is created. | Total Cash Flow!X1:Z4 contains [["",35599,38385],["Beg. Bal",5000,200],["End. Bal",11775,35160],["Change",6775,34960]], and a clustered column chart named 'CashBalanceComparison' is positioned at M2:T18 with title 'Cash Balance Comparison', categories [35599,38385], and the three corresponding series. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
// Remove a chart with the target name if one remains from an earlier attempt.
const existingChart = sheet.charts.getItemOrNullObject("CashBalanceComparison");
existingChart.load("isNullObject");
await ... | null | outputs/train/8ba8540dff0a803d6c867da7952d3fbe35a31023b9d8c97ba31258244ee975b1/gold.xlsx | Total Cash Flow!X1:Z4 contains [["",35599,38385],["Beg. Bal",5000,200],["End. Bal",11775,35160],["Change",6775,34960]], and a clustered column chart named 'CashBalanceComparison' is positioned at M2:T18 with title 'Cash Balance Comparison', categories [35599,38385], and the three corresponding series.
| 2026-09-08T09:55:41.960284+00:00 | Chart | 6,894 | 0 | 5 | 3 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Create a compact source table for the three requested measures and two IDs.\n const source = sheet.getRange(\"M20:O23\");\n source.formulas = [\n [\"Metric\", \"35599\", \"3838... | 8ba8540dff0a803d6c867da7952d3fbe35a31023b9d8c97ba31258244ee975b1 | workbooks/Backward_EnronFuse.1119-File.xlsx | 7ae259bee93c3f065170dbd1f9d97a2306778a4f31ee6396d514e58f844a2911 | 1501013915919ea34933af168bd90688b2f2aa9d590a1c7b1a7002a10ea2d902 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1121-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1121-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, create a clustered column chart comparing Beg. Bal, End. Bal, and Change for 35599 and 38385. Title it “Cash Balance Comparison” and place it to the right of the existing data. | The three metrics are stored on nonadjacent rows (Beg. Bal in row 3, End. Bal in row 5, and Change in row 7), while the two comparison categories are in I1:J1. Creating a chart and adding each metric as a separate series directly supports this noncontiguous layout. | A new clustered column chart named “CashBalanceComparison” on 'Total Cash Flow', positioned at M2:T18 and titled “Cash Balance Comparison”, with categories [35599, 38385] and three series: Beg. Bal = [5000, 200], End. Bal = [11775, 35160], and Change = [6775, 34960]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const oldChart = sheet.charts.getItemOrNullObject("CashBalanceComparison");
oldChart.load("isNullObject");
await context.sync();
if (!oldChart.isNullObject) {
oldChart.delete();
await context.sy... | null | outputs/train/fc7b7261f6e471efd1d4436157a39602ce180d1e0aa6ca6912ce1c0116cc1406/gold.xlsx | A new clustered column chart named “CashBalanceComparison” on 'Total Cash Flow', positioned at M2:T18 and titled “Cash Balance Comparison”, with categories [35599, 38385] and three series: Beg. Bal = [5000, 200], End. Bal = [11775, 35160], and Change = [6775, 34960].
| 2026-09-08T09:56:02.689241+00:00 | Chart | 7,047 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n const charts = sheet.charts;\n const prior = charts.getItemOrNullObject(\"CashBalanceComparison\");\n prior.load(\"isNullObject\");\n await context.sync();\n if (!prior.isNullObject)... | fc7b7261f6e471efd1d4436157a39602ce180d1e0aa6ca6912ce1c0116cc1406 | workbooks/Backward_EnronFuse.1121-File.xlsx | c8b152be5166f33e25c28b5430dc56da6023bbc23d61116dc923253f82b2baf3 | 857399cbeded49f622d2bbac068469516350b1c15510bc5f45d60f850234f081 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1125-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1125-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, create a clustered column chart comparing 1999 Cash (Net), 2000 Oper/Rem/Gas/Finc, and 2000 Personal. Title it “Cash Flow Comparison” and place it in E10:K26. | The 'Total Cash Flow' sheet lists three key cash-flow figures in A11:C15: 1999 Cash (Net), 2000 Oper/Rem/Gas/Finc, and 2000 Personal. A clustered column chart can be created directly from that range to compare the positive and negative amounts visually. | A new clustered column chart named "CashFlowComparison" on 'Total Cash Flow', sourced from A11:C15, titled "Cash Flow Comparison", and positioned from E10 to K26. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const sourceRange = sheet.getRange("A11:C15");
const existingChart = sheet.charts.getItemOrNullObject("CashFlowComparison");
existingChart.load("isNullObject");
await context.sync();
if (!existingCha... | null | outputs/train/7a77f46ef028f7f7971600a17609f4bd3825612048352ffde5d7896011bd3a8e/gold.xlsx | A new clustered column chart named "CashFlowComparison" on 'Total Cash Flow', sourced from A11:C15, titled "Cash Flow Comparison", and positioned from E10 to K26.
| 2026-09-08T09:56:12.015827+00:00 | Chart | 7,249 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Create a compact, contiguous source table for the three requested cash-flow items.\n const source = sheet.getRange(\"M1:N4\");\n source.values = [\n [\"Cash Flow Item\", \"Amou... | 7a77f46ef028f7f7971600a17609f4bd3825612048352ffde5d7896011bd3a8e | workbooks/Backward_EnronFuse.1125-File.xlsx | 3a70e82b3179adfd01f026608d0b9fc0314078bc58f16c62510921f5723ad4d0 | c6cd686b8f9ed0c80c625a9d3900595a94fa254cec6fea1fb0bd5faf5c74ffa7 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1120-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1120-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a compact helper table in M1:N9 using only the eight rows where both A and C contain values, preserving their original order. Use the headers Item and Amount, and make every helper-table data cell a direct formula link to its source cell rather than a copied value. From that helpe... | The eight usable source records are the noncontiguous rows 2, 3, 5, 7, 9, 11, 13, and 15, where both columns A and C contain values. A compact linked helper table in M:N is needed before adding the chart. The chart then requires explicit series, label, point-color, naming, and positioning configuration. | 'Total Cash Flow'!M1:N9 contains [["Item","Amount"],["=A2","=C2"],["=A3","=C3"],["=A5","=C5"],["=A7","=C7"],["=A9","=C9"],["=A11","=C11"],["=A13","=C13"],["=A15","=C15"]], displaying Partnership Agreements/-2500, Earnest Money/-6500, Cash at Closing/-225000, Loan from Gary/25000, Initial Working Cap./-5000, 1999 Cash (... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const helper = sheet.getRange("M1:N9");
helper.formulas = [
["Item", "Amount"],
["=A2", "=C2"],
["=A3", "=C3"],
["=A5", "=C5"],
["=A7", "=C7"],
["=A9", "=C9"],
["=A11", "=C11"],
... | null | outputs/train/7ba8eba97d483daab5fd60299872846e653f02bc04a738da6149fa782c4b9ad0/gold.xlsx | 'Total Cash Flow'!M1:N9 contains [["Item","Amount"],["=A2","=C2"],["=A3","=C3"],["=A5","=C5"],["=A7","=C7"],["=A9","=C9"],["=A11","=C11"],["=A13","=C13"],["=A15","=C15"]], displaying Partnership Agreements/-2500, Earnest Money/-6500, Cash at Closing/-225000, Loan from Gary/25000, Initial Working Cap./-5000, 1999 Cash (... | 2026-09-08T09:56:47.311601+00:00 | Chart | 6,994 | 1 | 5 | 2 | 1 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n const r = sheet.getRange(\"A1:C38\");\n r.load(\"values\");\n await context.sync();\n const found = [];\n for (let i = 0; i < r.values.length; i++) {\n if (r.values[i][0] !== \"\"... | 7ba8eba97d483daab5fd60299872846e653f02bc04a738da6149fa782c4b9ad0 | workbooks/Backward_EnronFuse.1120-File.xlsx | c8b152be5166f33e25c28b5430dc56da6023bbc23d61116dc923253f82b2baf3 | 45020f24d1f83bd12b4107864e9a167d60fd2f123f4d10e2d3c263aabdaf94ec | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1126-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1126-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart comparing the 1999 and 2000 beginning balance, ending balance, and change figures in G1:J7. Title it 'Cash Balance Comparison' and place it in M2:T18. | The worksheet contains 1999 and 2000 beginning balances, ending balances, and changes in G1:J7. A clustered column chart can compare those three cash-flow measures across the two years. | Total Cash Flow contains a clustered column chart named 'CashBalanceComparison' with source range G1:J7, title 'Cash Balance Comparison', and position M2:T18. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("CashBalanceComparison");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.dele... | null | outputs/train/91dcfca963bbf85209a7c1d6c75a8bcb019672a9ea85abb515089f76e5d46f0f/gold.xlsx | Total Cash Flow contains a clustered column chart named 'CashBalanceComparison' with source range G1:J7, title 'Cash Balance Comparison', and position M2:T18.
| 2026-09-08T09:57:08.342618+00:00 | Chart | 7,300 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Correct the second series header so the chart compares 1999 with 2000.\n sheet.getRange(\"J2\").values = [[2000]];\n\n // Remove a prior copy if this task has already been run.\n ... | 91dcfca963bbf85209a7c1d6c75a8bcb019672a9ea85abb515089f76e5d46f0f | workbooks/Backward_EnronFuse.1126-File.xlsx | 3a70e82b3179adfd01f026608d0b9fc0314078bc58f16c62510921f5723ad4d0 | 0a99ce15383b7ef714184fecd54f7e92f05213f97cf665972334638b6eabba75 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1128-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1128-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart comparing 1999 Cash (Net), 2000 Oper/Rem/Gas/Finc, and 2000 Personal using their values in column C. Title it 'Cash Flow Comparison' and place it from M2 to T18. | The worksheet contains three labeled cash-flow figures in A11:C15: 1999 Cash (Net) = 41,735, 2000 Oper/Rem/Gas/Finc = 14,628, and 2000 Personal = -25,704. A clustered column chart makes their positive and negative values easy to compare and requires adding a chart to the worksheet. | A new clustered column chart named 'CashFlowComparison' on 'Total Cash Flow', positioned from M2 to T18, titled 'Cash Flow Comparison', and sourced from A11:C15 to plot 1999 Cash (Net) = 41735, 2000 Oper/Rem/Gas/Finc = 14628, and 2000 Personal = -25704. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const sourceRange = sheet.getRange("A11:C15");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sourceRange,
Excel.ChartSeriesBy.columns
);
chart.name = "CashFlowComparison";... | null | outputs/train/aabd31371cf6fc39d3d0af4d7c0317e9d5639fb8ea548d01ec4a54da2c395cb7/gold.xlsx | A new clustered column chart named 'CashFlowComparison' on 'Total Cash Flow', positioned from M2 to T18, titled 'Cash Flow Comparison', and sourced from A11:C15 to plot 1999 Cash (Net) = 41735, 2000 Oper/Rem/Gas/Finc = 14628, and 2000 Personal = -25704.
| 2026-09-08T09:57:27.736770+00:00 | Chart | 7,402 | 1 | 5 | 1 | 0 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Create a compact source range using the requested labels and their column C values.\n const source = sheet.getRange(\"V2:W5\");\n source.values = [\n [\"Cash Flow Category\", \... | aabd31371cf6fc39d3d0af4d7c0317e9d5639fb8ea548d01ec4a54da2c395cb7 | workbooks/Backward_EnronFuse.1128-File.xlsx | ad73614346b8ebc101f7b0141550acd3e10e4f5978b6f6602b2aff25e59e868c | 7b14f824268fcbd4107eec0bfb792b60b215d8426ad9027ae36066b329c5d987 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1126-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1126-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart titled 'Beginning vs Ending Cash Balance' that compares Beginning Balance and Ending Balance for accounts 35599 and 38385. Place it at M2:T18. | The account IDs are in I1:J1, with beginning balances in I3:J3 and ending balances in I5:J5. A new clustered column chart can use the IDs as categories, while explicitly adding the two balance rows as separate series. | A new clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow' at M2:T18, titled 'Beginning vs Ending Cash Balance', with categories 35599 and 38385 and two series: 'Beginning Balance' = [5000, 200] and 'Ending Balance' = [11775, 35160]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sheet.getRange("I1:J1"),
Excel.ChartSeriesBy.rows
);
chart.name = "CashBalanceComparison";
chart.setPosition("M2", "T18");
... | null | outputs/train/edcd1e5a5c5c7afe2bd7ec47f5f90946f162cd7c974011e2aefaa21791cd0fbf/gold.xlsx | A new clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow' at M2:T18, titled 'Beginning vs Ending Cash Balance', with categories 35599 and 38385 and two series: 'Beginning Balance' = [5000, 200] and 'Ending Balance' = [11775, 35160].
| 2026-09-08T09:57:31.637090+00:00 | Chart | 7,302 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n\n for (const existing of charts.items) {\n existing.title.load(\"text\");\n }\n await contex... | edcd1e5a5c5c7afe2bd7ec47f5f90946f162cd7c974011e2aefaa21791cd0fbf | workbooks/Backward_EnronFuse.1126-File.xlsx | 3a70e82b3179adfd01f026608d0b9fc0314078bc58f16c62510921f5723ad4d0 | 8a1c2ea86e965c0bb16aa966b072f018d06c0c013fb5579f55fcf2207e2f7075 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1101-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1101-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, build the regional outright forward curves in C2:G9 for SoCal, PG&E, Malin, EPSJ, and Permian by adding Nymex in column B to the corresponding basis differential in I:M. Use formulas that return #N/A when the matching basis cell is blank—for example, C2 should be =IF(I2="",NA(),$B2+I2)—and fill the logic acr... | The formulas derive outright regional prices from Nymex and the basis differentials. The chart then requires calculated Cal 5 ranking, tie-breaking by source-column order, explicit series remapping, and a trendline targeted to the curve with the greatest Q2-to-Cal 5 decline. The corrected code assigns the supported cat... | Sheet1!C1:G1 = ["SoCal","PG&E","Malin","EPSJ","Permian"]. Every cell in Sheet1!C2:G9 contains =IF(<matching I:M basis cell>="",NA(),$B<row>+<matching basis cell>); specifically, C2 = =IF(I2="",NA(),$B2+I2) and G9 = =IF(M9="",NA(),$B9+M9). The blank PG&E basis in J2 therefore produces #N/A in D2. Sheet1 contains a line-... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const headers = ["SoCal", "PG&E", "Malin", "EPSJ", "Permian"];
const basisColumns = ["I", "J", "K", "L", "M"];
sheet.getRange("C1:G1").values = [headers];
const formulas = [];
for (let row = 2; row <= 9; row+... | null | outputs/train/b1637416cf5bbaa7ee7e47084addc9fcb0463e7e7951b4844073fd0de0ecfb03/gold.xlsx | Sheet1!C1:G1 = ["SoCal","PG&E","Malin","EPSJ","Permian"]. Every cell in Sheet1!C2:G9 contains =IF(<matching I:M basis cell>="",NA(),$B<row>+<matching basis cell>); specifically, C2 = =IF(I2="",NA(),$B2+I2) and G9 = =IF(M9="",NA(),$B9+M9). The blank PG&E basis in J2 therefore produces #N/A in D2. Sheet1 contains a line-... | 2026-09-08T09:58:05.418778+00:00 | Chart | 5,926 | 0 | 5 | 5 | 2 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n const formulas = [];\n ... | b1637416cf5bbaa7ee7e47084addc9fcb0463e7e7951b4844073fd0de0ecfb03 | workbooks/Backward_EnronFuse.1101-File.xlsx | 93ef33702cfbb98cc3b066e7a1e83fa932a2806839761dd02a3300b1e4fb46eb | 0b0cc1ed0a1dd0b77831ccf66f6280f667b5a03c592a5f26ec041504fcfd043d | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1128-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1128-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart comparing the beginning and ending balances for properties 35599 and 38385. Use the property numbers as the category labels, name the series 'Beginning Balance' and 'Ending Balance', title the chart 'Beginning vs. Ending Cash Balance', and place it over E1... | The worksheet contains property IDs 35599 and 38385 in I1:J1, with beginning balances in I3:J3 and ending balances in I5:J5. Creating a blank clustered-column chart and explicitly adding two series uses the series collection’s add capability to compare those balances. | A clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow' at E10:K26, titled 'Beginning vs. Ending Cash Balance', with categories 35599 and 38385; 'Beginning Balance' values 5000 and 200; and 'Ending Balance' values 11775 and 35160. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const charts = sheet.charts;
const existingChart = charts.getItemOrNullObject("CashBalanceComparison");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
... | null | outputs/train/c411258dfac297a142d9c731222f7729294bd0542ee84d7daa292a714094ba3c/gold.xlsx | A clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow' at E10:K26, titled 'Beginning vs. Ending Cash Balance', with categories 35599 and 38385; 'Beginning Balance' values 5000 and 200; and 'Ending Balance' values 11775 and 35160.
| 2026-09-08T09:58:06.102496+00:00 | Chart | 7,404 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n const charts = sheet.charts;\n\n const chart = charts.add(\n Excel.ChartType.columnClustered,\n sheet.getRange(\"I1:J1\"),\n Excel.ChartSeriesBy.rows\n );\n chart.name = \"Be... | c411258dfac297a142d9c731222f7729294bd0542ee84d7daa292a714094ba3c | workbooks/Backward_EnronFuse.1128-File.xlsx | ad73614346b8ebc101f7b0141550acd3e10e4f5978b6f6602b2aff25e59e868c | bab5818d1fb439186d257949cb231479e375eda94d7f437f53a12b8981e13f93 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1130-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1130-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart using the cash-flow descriptions in A2:A15 as category labels and the amounts in C2:C15 as values. Place it below the data from E40 to K58, title it 'Cash Flow Amounts with Linear Trend', hide the legend, and add a linear trendline to the amount series. | The sheet contains cash-flow descriptions in A2:A15 and corresponding amounts in C2:C15. A clustered column chart can visualize those amounts, and adding a linear trendline to its single series directly exercises the trendline-creation API. | Total Cash Flow contains a chart named CashFlowAmountsTrendChart at E40:K58, titled 'Cash Flow Amounts with Linear Trend', with categories from A2:A15, values from C2:C15, no legend, and one linear trendline. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const valueRange = sheet.getRange("C2:C15");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
valueRange,
Excel.ChartSeriesBy.columns
);
chart.name = "CashFlowAmountsTrendCh... | null | outputs/train/258cf829edef980ebf3ca598cbe5b3a2e6d2a54c3c133ee37c574f49d86a18a7/gold.xlsx | Total Cash Flow contains a chart named CashFlowAmountsTrendChart at E40:K58, titled 'Cash Flow Amounts with Linear Trend', with categories from A2:A15, values from C2:C15, no legend, and one linear trendline.
| 2026-09-08T09:58:07.863743+00:00 | Chart | 7,558 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n const chart = sheet.charts.add(\n Excel.ChartType.columnClustered,\n sheet.getRange(\"A2:C15\"),\n Excel.ChartSeriesBy.columns\n );\n\n chart.name = \"CashFlowAmountsWithLin... | 258cf829edef980ebf3ca598cbe5b3a2e6d2a54c3c133ee37c574f49d86a18a7 | workbooks/Backward_EnronFuse.1130-File.xlsx | ad73614346b8ebc101f7b0141550acd3e10e4f5978b6f6602b2aff25e59e868c | d2541362101416d8e90fe9238072eaaa0f26e14294fc5bdf495d50c45859ad99 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1108-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1108-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | Complete and verify the regional basis-differential section on Sheet1 by calculating each region’s price minus Nymex for every listed term, including the missing PG&E result for Feb-Mar. Use formulas rather than pasted differential values, match regions by their headers, and preserve the existing term order. Then creat... | The duplicate regional headers identify the outright-price columns and corresponding basis-differential columns. The solution must preserve the displayed regional prices to avoid circular references, replace every differential with a regional-price-minus-Nymex formula, validate the missing PG&E result, and add five ind... | Sheet1!I2:M9 contains formulas calculating each matched regional price minus Nymex, including Sheet1!J2 = =D2-$B2 with calculated value 0.00. Sheet1 contains exactly one chart internally named chtRegionalBasis at O2:W18, titled “Regional Basis Differentials by Term,” with term categories in their existing order and fiv... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const used = sheet.getUsedRange();
used.load(["values", "rowCount", "columnCount"]);
await context.sync();
const normalize = (value) => String(value ?? "").trim();
const headers = used.values[0].map(normalize)... | null | outputs/train/76d5d76c9ecf04aa9431cc24ed1efc27a87d6697097bf102ad8653b32f917d5e/gold.xlsx | Sheet1!I2:M9 contains formulas calculating each matched regional price minus Nymex, including Sheet1!J2 = =D2-$B2 with calculated value 0.00. Sheet1 contains exactly one chart internally named chtRegionalBasis at O2:W18, titled “Regional Basis Differentials by Term,” with term categories in their existing order and fiv... | 2026-09-08T09:58:30.334169+00:00 | Chart | 6,486 | 2 | 5 | 3 | 1 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n\n // Calculate each regional basis by matching the differential header to the price header.\n const differentialRange = sheet.getRange(\"I2:M9\");\n const formulas = [];\n const outputColumns ... | 76d5d76c9ecf04aa9431cc24ed1efc27a87d6697097bf102ad8653b32f917d5e | workbooks/Backward_EnronFuse.1108-File.xlsx | 1b325b90844318e461911d1abfd4e4aad7275da71eeaa69440a3e2aa26d3b468 | f5de3f9d9834d5a448194c2a3848d54cf3b4cd85c11d219bcbdcd72c519d5ee7 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1122-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1122-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, build a linked comparison table in M1:O3 using the date headings from I1:J1, the beginning balances from I3:J3, and the ending balances from I5:J5. Then create a clustered column chart from that table with the two dates on the horizontal axis and Beginning Balance and Ending Balance as the... | The workbook contains two dated balance columns in I and J, with beginning balances in row 3 and ending balances in row 5. Building a linked, contiguous summary table makes those separated cells suitable for a two-series chart and requires deliberate series orientation when creating it. | Total Cash Flow!M1:O3 contains formulas/values [["Balance",35599,38385],["Beginning Balance",5000,200],["Ending Balance",11775,35160]], and Q2:X18 contains a clustered column chart named BalanceComparisonChart titled “Beginning vs Ending Cash Balance”, with date categories 35599 and 38385, series Beginning Balance = [5... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const charts = sheet.charts;
const existingChart = charts.getItemOrNullObject("BalanceComparisonChart");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
... | null | outputs/train/68b7e39d5d3b0f505ffc7ed8c1e84da9bd83c608de9491d587f73c6a8b43bef3/gold.xlsx | Total Cash Flow!M1:O3 contains formulas/values [["Balance",35599,38385],["Beginning Balance",5000,200],["Ending Balance",11775,35160]], and Q2:X18 contains a clustered column chart named BalanceComparisonChart titled “Beginning vs Ending Cash Balance”, with date categories 35599 and 38385, series Beginning Balance = [5... | 2026-09-08T09:58:32.120595+00:00 | Chart | 7,096 | 1 | 5 | 2 | 1 | 0.2 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Build a linked comparison table.\n sheet.getRange(\"M1:O3\").clear(Excel.ClearApplyTo.all);\n sheet.getRange(\"M1:O3\").formulas = [\n [\"\", \"=I1\", \"=J1\"],\n [\"Beginni... | 68b7e39d5d3b0f505ffc7ed8c1e84da9bd83c608de9491d587f73c6a8b43bef3 | workbooks/Backward_EnronFuse.1122-File.xlsx | c8b152be5166f33e25c28b5430dc56da6023bbc23d61116dc923253f82b2baf3 | 3de85547cf19f82055f5856d10cf54119b1c255e59ebc728349a738380447b24 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1131-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1131-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart comparing Beg. Bal, End. Bal, and Change for 35599 and 38385 using the values in I3:J3, I5:J5, and I7:J7. Title it 'Cash Balances by Account', name the chart 'CashBalanceComparison', and place it from M2 to T18. | The worksheet contains two cash-flow identifiers in I1:J1 (35599 and 38385) and corresponding beginning balance, ending balance, and change figures in rows 3, 5, and 7. A clustered column chart can start with the beginning-balance series and then explicitly add the ending-balance and change series. | A new clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow', positioned at M2:T18 and titled 'Cash Balances by Account', with categories 35599 and 38385 and series: Beg. Bal = [5000, 200], End. Bal = [11775, 35160], and Change = [6775, 34960]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const existingChart = sheet.charts.getItemOrNullObject("CashBalanceComparison");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
existingChart.delete();
... | null | outputs/train/d73c2a593f6b43875e5572f6965059ac76526e76c5bff3538a99ced0cbded985/gold.xlsx | A new clustered column chart named 'CashBalanceComparison' on 'Total Cash Flow', positioned at M2:T18 and titled 'Cash Balances by Account', with categories 35599 and 38385 and series: Beg. Bal = [5000, 200], End. Bal = [11775, 35160], and Change = [6775, 34960].
| 2026-09-08T09:58:47.839767+00:00 | Chart | 7,608 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Remove an existing chart with the requested name, if present.\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n const existing = charts.ite... | d73c2a593f6b43875e5572f6965059ac76526e76c5bff3538a99ced0cbded985 | workbooks/Backward_EnronFuse.1131-File.xlsx | ad73614346b8ebc101f7b0141550acd3e10e4f5978b6f6602b2aff25e59e868c | 21b1b2a6e7efe91de2125be05685ca39ded9da370fa5e0f5b6461e1da7cce333 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1133-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1133-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the 'Total Cash Flow' sheet, create a clustered column chart comparing the amounts for the listed items from Partnership Agreements through 2000 Personal. Title it 'Selected Cash Flow Items', hide the legend, and place it from E10 to K27. | The worksheet contains named cash-flow items in column A and their positive or negative amounts in column C. A clustered column chart created from A2:C15 provides a clear visual comparison of these actual entries. | A chart named 'CashFlowItemsChart' on 'Total Cash Flow', positioned at E10:K27, titled 'Selected Cash Flow Items', with no legend, plotting Partnership Agreements = -2500, Earnest Money = -6500, Cash at Closing = -225000, Loan from Gary = 25000, Initial Working Cap. = -5000, 1999 Cash (Net) = 41735, 2000 Oper/Rem/Gas/F... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const sourceRange = sheet.getRange("A2:C15");
const chart = sheet.charts.add(
Excel.ChartType.columnClustered,
sourceRange,
Excel.ChartSeriesBy.columns
);
chart.name = "CashFlowItemsChart";... | null | outputs/train/5c2418ebf9a370927bde85da2a070b62949b3d8b5f3d2b41a7013c35a07e1be7/gold.xlsx | A chart named 'CashFlowItemsChart' on 'Total Cash Flow', positioned at E10:K27, titled 'Selected Cash Flow Items', with no legend, plotting Partnership Agreements = -2500, Earnest Money = -6500, Cash at Closing = -225000, Loan from Gary = 25000, Initial Working Cap. = -5000, 1999 Cash (Net) = 41735, 2000 Oper/Rem/Gas/F... | 2026-09-08T09:58:51.786768+00:00 | Chart | 7,708 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Remove a prior copy of this chart, if present.\n const existingChart = sheet.charts.getItemOrNullObject(\"SelectedCashFlowItemsChart\");\n existingChart.load(\"isNullObject\");\n ... | 5c2418ebf9a370927bde85da2a070b62949b3d8b5f3d2b41a7013c35a07e1be7 | workbooks/Backward_EnronFuse.1133-File.xlsx | 0939e1716a775adee43d1c013e8cd377a2ab263169a803b27f1974510e05a936 | 1d5953336cf3ca87623ea1377ab1a3851f7bfc2b76038d6a7aee882bb2f1afe4 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1118-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1118-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On 'Total Cash Flow', create a live reconciliation table in M2:P5 with the exact headers Metric, Column I, Column J, and Reconciliation Status. Use formulas that match Beg. Bal, End. Bal, and Change from the labels in column G instead of assuming their row numbers. Link the Beg. Bal and End. Bal amounts to their corres... | The reconciliation must discover source rows from the labels in column G, link beginning and ending balances with formulas, calculate Change independently, and validate it against both the source Change row and its column-K total. The chart is then created from the first three table columns and explicitly configured. T... | 'Total Cash Flow'!M2:P5 displays [["Metric","Column I","Column J","Reconciliation Status"],["Beg. Bal",5000,200,"OK"],["End. Bal",11775,35160,"OK"],["Change",6775,34960,"OK"]]. M3:M5 contain label-matching formulas against column G; N3:O4 link by formula to the matched I/J source cells; N5:O5 calculate End. Bal minus B... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
// Replace a chart only when its object name is exactly the required name.
const existing = sheet.charts.getItemOrNullObject("BalanceReconciliationChart");
existing.load("isNullObject");
await context.s... | null | outputs/train/fa8d63506812be86cc11c4f036f67615960a0004573e5705c8cd6c938506a057/gold.xlsx | 'Total Cash Flow'!M2:P5 displays [["Metric","Column I","Column J","Reconciliation Status"],["Beg. Bal",5000,200,"OK"],["End. Bal",11775,35160,"OK"],["Change",6775,34960,"OK"]]. M3:M5 contain label-matching formulas against column G; N3:O4 link by formula to the matched I/J source cells; N5:O5 calculate End. Bal minus B... | 2026-09-08T09:59:43.999563+00:00 | Chart | 6,841 | 0 | 5 | 4 | 2 | 0 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
... | fa8d63506812be86cc11c4f036f67615960a0004573e5705c8cd6c938506a057 | workbooks/Backward_EnronFuse.1118-File.xlsx | 7ae259bee93c3f065170dbd1f9d97a2306778a4f31ee6396d514e58f844a2911 | d259347ba126dc44d2328b2e966693c5570fced7180646686d7e12a994951731 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.1112-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.1112-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a formula-driven comparison table in Q2:T6; do not create another worksheet or use any other helper range. Leave Q2 blank and derive the three headers in R2:T2 from the existing Summer, Winter Average, and Winter Peak headings. Include N & S Chehalis, Total Supply, Sumas Receipts, and Roosevelt North ... | The source data is split between Summer column C and Winter columns E:F, and the requested order requires comparing Winter Peak values before building the table. Creating the chart initially from Q2:S6 and subsequently extending that same chart with the T-column series specifically exercises adding a range-backed serie... | Sheet1!Q2:T6 has formulas [["","=$C$2","=$E$1&\" \"&$E$2","=$E$1&\" \"&$F$2"],["=$A$9","=$C$9","=$E$9","=$F$9"],["=$B$6&\" \"&$A$3","=SUM($C$4:$C$5)","=$E$6","=$F$6"],["=$A$4","=$C$4","=$E$4","=$F$4"],["=$A$5","=$C$5","=$E$5","=$F$5"]], displaying [["","Summer","Winter Average","Winter Peak"],["N & S Chehalis",850,1175... | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const previous = sheet.charts.getItemOrNullObject("SummerWinterComparison");
previous.load("isNullObject");
await context.sync();
if (!previous.isNullObject) {
previous.delete();
await context.sync();
... | null | outputs/train/695d7a48e06e546188d3d9da0c5109503c502cb32c9dca7b9942a94fb9f06e0b/gold.xlsx | Sheet1!Q2:T6 has formulas [["","=$C$2","=$E$1&\" \"&$E$2","=$E$1&\" \"&$F$2"],["=$A$9","=$C$9","=$E$9","=$F$9"],["=$B$6&\" \"&$A$3","=SUM($C$4:$C$5)","=$E$6","=$F$6"],["=$A$4","=$C$4","=$E$4","=$F$4"],["=$A$5","=$C$5","=$E$5","=$F$5"]], displaying [["","Summer","Winter Average","Winter Peak"],["N & S Chehalis",850,1175... | 2026-09-08T09:59:48.914809+00:00 | Chart | 6,537 | 2 | 5 | 6 | 2 | 0.4 | [
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "",
"success": false,
"attempts": 3,
"stdout": null
},
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const table = sheet.getRa... | 695d7a48e06e546188d3d9da0c5109503c502cb32c9dca7b9942a94fb9f06e0b | workbooks/Backward_EnronFuse.1112-File.xlsx | e8a19ec338bd43f1dbcbb07b1dff105a0471bfb1d3f8c2b31ad6502d9b58c6a6 | 94df05de03b9300624bba3687d9a1be2e271e9d7ad585ceeb8aeed1891bc445e | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartCollection.add::Backward_EnronFuse.1134-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartCollection.add | Backward_EnronFuse.1134-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, create a clustered column chart comparing the beginning balance, ending balance, and change amounts for columns 35599 and 38385. Title it “Cash Balance Comparison” and place it to the right of the existing data. | The worksheet contains beginning balance, ending balance, and change figures in rows 3, 5, and 7 for the two columns headed 35599 and 38385. A clustered column chart can be created directly from that comparison block and positioned beside the existing data. | A clustered column chart named 'CashBalanceComparisonChart' is created on 'Total Cash Flow', positioned at M2:T18, titled 'Cash Balance Comparison', and sourced from G3:J7. It charts the available values for 35599 (Beg. Bal 5000, End. Bal 11775, Change 6775) and 38385 (Beg. Bal 200, End. Bal 35160, Change 34960). | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("CashBalanceComparisonChart");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.... | null | outputs/train/fb2ac184e2890fd776de5d2623e167d27d220d257fe7dd51571d1cefbc6a82e2/gold.xlsx | A clustered column chart named 'CashBalanceComparisonChart' is created on 'Total Cash Flow', positioned at M2:T18, titled 'Cash Balance Comparison', and sourced from G3:J7. It charts the available values for 35599 (Beg. Bal 5000, End. Bal 11775, Change 6775) and 38385 (Beg. Bal 200, End. Bal 35160, Change 34960).
| 2026-09-08T09:59:53.215120+00:00 | Chart | 7,759 | 0 | 5 | 1 | 0 | 0 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n\n // Create a compact source table below the chart area using the workbook's existing values.\n const source = sheet.getRange(\"M30:O33\");\n source.values = [\n [\"Balance Type\", ... | fb2ac184e2890fd776de5d2623e167d27d220d257fe7dd51571d1cefbc6a82e2 | workbooks/Backward_EnronFuse.1134-File.xlsx | 0939e1716a775adee43d1c013e8cd377a2ab263169a803b27f1974510e05a936 | 13c025d20b4a75c66d5250a3b06747adf0e5439320b51e2352fe72b435aba9b5 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1126-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1126-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, create a line chart of the cash-flow entries from Partnership Agreements through 2000 Personal, using the labels in column A and amounts in column C. Name it “Cash Flow Trend”, add a linear trendline, title it “Cash Flow Amount Trend”, and place it over E10:K26. | A valid source range is required to create the chart, and the chart must always retain at least one series. The code keeps the first series generated from A2:C15, reconfigures it to use the labels in column A and cash-flow amounts in column C, deletes only additional series, and then adds and verifies one linear trendl... | A new chart named “Cash Flow Trend” on 'Total Cash Flow', positioned at E10:K26, titled “Cash Flow Amount Trend”, with exactly one line series named “Cash Flow Amount” using categories 'Total Cash Flow'!A2:A15 and values 'Total Cash Flow'!C2:C15, and exactly one linear trendline. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const sourceRange = sheet.getRange("A2:C15");
const chart = sheet.charts.add(
Excel.ChartType.line,
sourceRange,
Excel.ChartSeriesBy.columns
);
// Retain one generated series so the chart i... | null | outputs/train/398de2f536a19489e3ca1f72a15c061e4a7ab09942232caa818d2e8e651d31be/gold.xlsx | A new chart named “Cash Flow Trend” on 'Total Cash Flow', positioned at E10:K26, titled “Cash Flow Amount Trend”, with exactly one line series named “Cash Flow Amount” using categories 'Total Cash Flow'!A2:A15 and values 'Total Cash Flow'!C2:C15, and exactly one linear trendline.
| 2026-09-08T09:59:54.463031+00:00 | Chart | 7,303 | 2 | 5 | 3 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n const chart = sheet.charts.getItem(\"Cash Flow Trend\");\n const series = chart.series.getItemAt(0);\n series.trendlines.add(Excel.ChartTrendlineType.linear);\n await context.sync();\... | 398de2f536a19489e3ca1f72a15c061e4a7ab09942232caa818d2e8e651d31be | workbooks/Backward_EnronFuse.1126-File.xlsx | 3a70e82b3179adfd01f026608d0b9fc0314078bc58f16c62510921f5723ad4d0 | dc344695ba10e5f4fc36c3de2ab4f644baf9c256962c1a325962c181da4d62a8 | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartSeriesCollection.add::Backward_EnronFuse.113-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartSeriesCollection.add | Backward_EnronFuse.113-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On Sheet1, create a clustered column chart comparing Marketing revenues, Fin. energy pmts, Debt service/fixed pmt, and O&M for periods 7, 8, and 9. Title it “Periods 7-9 Revenue and Costs” and place it to the right of the existing data. | The period labels 7–9 are in Sheet1!K5:M5, with corresponding Marketing revenues, financial energy payments, debt service, and O&M data in rows 7–10. A clustered column chart can be created and populated by adding each row as a separate series. | Sheet1 contains a clustered column chart named “Periods_7_9_Revenue_and_Costs” positioned at O3:W18, titled “Periods 7-9 Revenue and Costs”, with categories 7, 8, 9 and four series: Marketing revenues = [100, 50, blank], Fin. energy pmts = [0, 0, 0], Debt service/fixed pmt = [40, 40, 40], and O&M = [9, 9, 9]. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Sheet1");
const charts = sheet.charts;
const existing = charts.getItemOrNullObject("Periods_7_9_Revenue_and_Costs");
existing.load("isNullObject");
await context.sync();
if (!existing.isNullObject) {
existing.delete... | null | outputs/train/66953283bb77c135154afe7ece2bb938e3638d1aa3ca86521c90cde74f3083b7/gold.xlsx | Sheet1 contains a clustered column chart named “Periods_7_9_Revenue_and_Costs” positioned at O3:W18, titled “Periods 7-9 Revenue and Costs”, with categories 7, 8, 9 and four series: Marketing revenues = [100, 50, blank], Fin. energy pmts = [0, 0, 0], Debt service/fixed pmt = [40, 40, 40], and O&M = [9, 9, 9].
| 2026-09-08T10:00:10.161665+00:00 | Chart | 7,506 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Sheet1\");\n const charts = sheet.charts;\n charts.load(\"items/name\");\n await context.sync();\n\n charts.items.forEach((c) => {\n if (c.name === \"Periods_7_9_Revenue_and_Costs\") c.delete();\n });\... | 66953283bb77c135154afe7ece2bb938e3638d1aa3ca86521c90cde74f3083b7 | workbooks/Backward_EnronFuse.113-File.xlsx | 251cd56c49f2ce3fc0c655010c780a4b1574f8e4f4d64cb9e37c9188af542e77 | e244092635fc05b15daeb252dfe1fce4ee72f77d375475c5df865be5b7b114eb | workbooks/inputs.zip | workbooks/train_outputs.zip |
Excel.ChartTrendlineCollection.add::Backward_EnronFuse.1135-File.xlsx | Gpt56Reasoning | Gpt56Reasoning | hard_saved | Excel.ChartTrendlineCollection.add | Backward_EnronFuse.1135-File.xlsx | Generate a realistic query that requires creating a new Chart in the workbook, grounded in its actual contents. | On the Total Cash Flow sheet, create a line chart of the cash-flow entries in A2:A15 and their amounts in C2:C15. Title it “Cash Flow Amount Trend,” add a linear trendline, and place the chart over E10:K27. | The transaction labels in column A and corresponding cash-flow amounts in column C provide a grounded series for a new chart. A line chart with a linear trendline highlights the direction across entries such as Partnership Agreements, Cash at Closing, and the 1999/2000 cash items. | Total Cash Flow!CashFlowAmountTrend is a line chart positioned at E10:K27, plotting A2:A15 against C2:C15, with title "Cash Flow Amount Trend" and exactly 1 linear trendline. | await Excel.run(async (context) => {
const sheet = context.workbook.worksheets.getItem("Total Cash Flow");
const charts = sheet.charts;
const existingChart = charts.getItemOrNullObject("CashFlowAmountTrend");
existingChart.load("isNullObject");
await context.sync();
if (!existingChart.isNullObject) {
e... | null | outputs/train/a167a8f2432912b2cd18f5078d06c50125bc09f575abe6e6d15e3a3297118088/gold.xlsx | Total Cash Flow!CashFlowAmountTrend is a line chart positioned at E10:K27, plotting A2:A15 against C2:C15, with title "Cash Flow Amount Trend" and exactly 1 linear trendline.
| 2026-09-08T10:00:12.746714+00:00 | Chart | 7,813 | 2 | 5 | 1 | 0 | 0.4 | [
{
"code": "await Excel.run(async (context) => {\n const sheet = context.workbook.worksheets.getItem(\"Total Cash Flow\");\n const chart = sheet.charts.add(Excel.ChartType.line, sheet.getRange(\"C2:C15\"), Excel.ChartSeriesBy.columns);\n chart.name = \"CashFlowAmountTrend\";\n chart.title.text = \"Cash Flow ... | a167a8f2432912b2cd18f5078d06c50125bc09f575abe6e6d15e3a3297118088 | workbooks/Backward_EnronFuse.1135-File.xlsx | 0939e1716a775adee43d1c013e8cd377a2ab263169a803b27f1974510e05a936 | 36619b5455b74496392ed90989b5afd0c28dd9a8316db6d9e36c518ec8e1ba66 | workbooks/inputs.zip | workbooks/train_outputs.zip |
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