source_sample_idx int64 1 6.59k | context_id int64 1 6.59k | split stringclasses 1
value | query stringlengths 1.7k 16.5k | answer stringlengths 55 9.29k | context stringlengths 24 14.8k | output_entities listlengths 1 157 |
|---|---|---|---|---|---|---|
1 | 1 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "62", "datatype": "monetaryItemType"}, {"numeric_entity": "171", "datatype": "monetaryItemType"}, {"numeric_entity": "396", "datatype": "monetaryItemType"}, {"numeric_entity": "412", "datatype": "monetaryItemType"}, {"numeric_entity": "345", "datatype": "monetaryItemType"}, {"numeric_entity": "324",... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Provision for Income Taxes Provision for Income Taxes</td><td>For the years ended December 31</td></tr><tr><td></td><td>2024</td><td>2023</td><td>2022</td></tr><tr><td>Current</td><td></td... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "62"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "171"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "396"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "412"
},
{
"datatype": "moneta... |
2 | 2 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "8068", "datatype": "monetaryItemType"}, {"numeric_entity": "8060", "datatype": "monetaryItemType"}, {"numeric_entity": "8042", "datatype": "monetaryItemType"}] | As of December 31, 2024, 2023 and 2022, the Company had a liability of $ 8,068 , $ 8,060 and $ 8,042 , respectively, representing the December 31, 2024, 2023 and 2022 fair values, respectively, of outstanding Progressive Waste restricted share units which are expected to be cash settled. All remaining unvested Progress... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "8068"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "8060"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "8042"
}
] |
7 | 7 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "1.7", "datatype": "monetaryItemType"}, {"numeric_entity": "0.2", "datatype": "monetaryItemType"}, {"numeric_entity": "0.7", "datatype": "monetaryItemType"}, {"numeric_entity": "1.7", "datatype": "monetaryItemType"}, {"numeric_entity": "0.2", "datatype": "monetaryItemType"}, {"numeric_entity": "0.7"... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Years Ended December 31,</td></tr><tr><td></td><td></td><td>2024</td><td></td><td>2023</t... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "1.7"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "0.2"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "0.7"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "1.7"
},
{
"datatype": "monet... |
19 | 19 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "6019", "datatype": "monetaryItemType"}, {"numeric_entity": "—", "datatype": "monetaryItemType"}, {"numeric_entity": "95148", "datatype": "monetaryItemType"}, {"numeric_entity": "—", "datatype": "monetaryItemType"}, {"numeric_entity": "105393", "datatype": "monetaryItemType"}, {"numeric_entity": "10... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "6019"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "—"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "95148"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "—"
},
{
"datatype": "moneta... |
21 | 21 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "two", "datatype": "integerItemType"}] | The “Other” columns presented in the previous table, represent amounts that are not allocated to our two lines of business. The following provides additional information about the items included in the line of business results “Other” column for the periods indicated. | [
{
"datatype": "integerItemType",
"numeric_entity": "two"
}
] |
24 | 24 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "462", "datatype": "monetaryItemType"}, {"numeric_entity": "—", "datatype": "monetaryItemType"}, {"numeric_entity": "1", "datatype": "monetaryItemType"}, {"numeric_entity": "461", "datatype": "monetaryItemType"}] | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(in millions)</td><td>Amount</td></tr><tr><td>Balance at December 31, 2022</td><td>$</td><td>462</td><td></td></tr><tr><td>Additions</td><td>—</td><td></td></tr><tr><td>Balance at December 31, 2023</td><td>$</td><td>462</td><td></td></tr><tr>... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "462"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "—"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "1"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "461"
}
] |
27 | 27 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "3774239", "datatype": "monetaryItemType"}, {"numeric_entity": "3691066", "datatype": "monetaryItemType"}, {"numeric_entity": "3583978", "datatype": "monetaryItemType"}, {"numeric_entity": "564602", "datatype": "monetaryItemType"}, {"numeric_entity": "164489", "datatype": "monetaryItemType"}, {"nume... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>2024</td><td></td><td>2023</td><td></td><td>2022</td></tr><tr><td>Reconciliation of real estate, at cost:</td><td></td><td></... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "3774239"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "3691066"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "3583978"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "564602"
},
{
"da... |
29 | 29 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "3.20", "datatype": "percentItemType"}, {"numeric_entity": "3.26", "datatype": "percentItemType"}, {"numeric_entity": "3.21", "datatype": "percentItemType"}, {"numeric_entity": "2.52", "datatype": "percentItemType"}, {"numeric_entity": "2.62", "datatype": "percentItemType"}, {"numeric_entity": "2.75... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>2024</td><td></td><td>2023</td><td></td><td>2022</td></tr><tr><td>PPL</td><td>3.20</td><td>%</td><td></td><td>3.26</td><td>%<... | [
{
"datatype": "percentItemType",
"numeric_entity": "3.20"
},
{
"datatype": "percentItemType",
"numeric_entity": "3.26"
},
{
"datatype": "percentItemType",
"numeric_entity": "3.21"
},
{
"datatype": "percentItemType",
"numeric_entity": "2.52"
},
{
"datatype": "perce... |
31 | 31 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "249.3", "datatype": "monetaryItemType"}, {"numeric_entity": "208.8", "datatype": "monetaryItemType"}, {"numeric_entity": "167.6", "datatype": "monetaryItemType"}, {"numeric_entity": "272.2", "datatype": "monetaryItemType"}, {"numeric_entity": "255.5", "datatype": "monetaryItemType"}, {"numeric_enti... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Years Ended December 31,</td></tr><tr><td></td><td></td><td>2024</td><td></td><td>2023</t... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "249.3"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "208.8"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "167.6"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "272.2"
},
{
"datatype"... |
37 | 37 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "5104017", "datatype": "sharesItemType"}, {"numeric_entity": "67917432", "datatype": "sharesItemType"}] | Immediately prior to the completion of our IPO, all of our then-outstanding shares of convertible preferred stock were automatically converted into 5,104,017 and 67,917,432 shares of our Class A and Class B common stock, respectively. | [
{
"datatype": "sharesItemType",
"numeric_entity": "5104017"
},
{
"datatype": "sharesItemType",
"numeric_entity": "67917432"
}
] |
58 | 58 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "30.4", "datatype": "monetaryItemType"}, {"numeric_entity": "67.1", "datatype": "monetaryItemType"}, {"numeric_entity": "58.9", "datatype": "monetaryItemType"}, {"numeric_entity": "82.2", "datatype": "monetaryItemType"}, {"numeric_entity": "134.1", "datatype": "monetaryItemType"}, {"numeric_entity":... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>December 31,</td></tr><tr><td></td><td></td><td>2024</td><td></td><td>2023</td></tr><tr><td>CURRENT ASSETS</td><td></td><td></td><td></td><td><... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "30.4"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "67.1"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "58.9"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "82.2"
},
{
"datatype": "m... |
62 | 62 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "464170000", "datatype": "sharesItemType"}, {"numeric_entity": "1000000", "datatype": "sharesItemType"}] | - Payment of dividends on our common stock is also subject to the prior payment of dividends on our 24 series of preferred stock and one series of senior preferred stock, representing an aggregate of 464,170,000 shares and 1,000,000 shares outstanding, respectively, as of December 31, 2024. Payment of dividends on all ... | [
{
"datatype": "sharesItemType",
"numeric_entity": "464170000"
},
{
"datatype": "sharesItemType",
"numeric_entity": "1000000"
}
] |
63 | 63 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "357", "datatype": "monetaryItemType"}, {"numeric_entity": "252", "datatype": "monetaryItemType"}, {"numeric_entity": "21", "datatype": "monetaryItemType"}, {"numeric_entity": "24", "datatype": "monetaryItemType"}, {"numeric_entity": "29", "datatype": "monetaryItemType"}, {"numeric_entity": "35", "d... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>December 31,</td></tr><tr><td>(in millions)</td><td>2024</td><td></td><td>2023</td></tr><tr><td>Deferred tax assets:</td><td></td><td></td><td></td></tr><tr><td>Net operating loss... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "357"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "252"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "21"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "24"
},
{
"datatype": "monetar... |
88 | 88 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "1.25", "datatype": "monetaryItemType"}, {"numeric_entity": "100", "datatype": "monetaryItemType"}] | Issuances under the PPL Capital Funding and RIE commercial paper programs are supported by the PPL Capital Funding syndicated credit facility, which, at December 31, 2024, had a total capacity of $ 1.25 billion and under which they are both borrowers. PPL Capital Funding’s Commercial paper program is also backed by a s... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "1.25"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "100"
}
] |
97 | 97 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "36", "datatype": "monetaryItemType"}, {"numeric_entity": "43", "datatype": "monetaryItemType"}, {"numeric_entity": "11", "datatype": "monetaryItemType"}, {"numeric_entity": "12", "datatype": "monetaryItemType"}, {"numeric_entity": "6", "datatype": "monetaryItemType"}, {"numeric_entity": "8", "datat... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(in millions)</td><td></td><td>Investments in Unconsolidated VIEs</td><td></td><td>Maximum Exposure to Loss</td></tr><tr><td>NQ Fund V</td><td></td><td>$</td><td... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "36"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "43"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "11"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "12"
},
{
"datatype": "monetaryI... |
107 | 107 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "79.8", "datatype": "monetaryItemType"}] | and $ 79.8 million, respectively, | [
{
"datatype": "monetaryItemType",
"numeric_entity": "79.8"
}
] |
113 | 113 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "485", "datatype": "monetaryItemType"}, {"numeric_entity": "499", "datatype": "monetaryItemType"}, {"numeric_entity": "480", "datatype": "monetaryItemType"}] | The Company sponsors a 401(k) retirement savings plan covering all eligible employees. The Company makes a discretionary matching contribution on a portion of employee participant salaries and, based on its profitability, may make an additional discretionary contribution at each fiscal year end to all eligible employee... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "485"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "499"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "480"
}
] |
124 | 124 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "13883", "datatype": "monetaryItemType"}, {"numeric_entity": "15357", "datatype": "monetaryItemType"}, {"numeric_entity": "15569", "datatype": "monetaryItemType"}, {"numeric_entity": "15737", "datatype": "monetaryItemType"}, {"numeric_entity": "14310", "datatype": "monetaryItemType"}, {"numeric_enti... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Operating Leases</td></tr><tr><td>2025</td><td>$</td><td>13,883</td><td></td></tr><tr><td>2026</td><td>15,357</td><td></td></tr><tr><td>2027</td><td>15,569</td><td></td></tr><tr><td>2028</td><td>15,737</td><td></td></tr><tr><td>2029<... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "13883"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "15357"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "15569"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "15737"
},
{
"datatype"... |
137 | 137 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "—", "datatype": "monetaryItemType"}, {"numeric_entity": "94", "datatype": "monetaryItemType"}, {"numeric_entity": "96", "datatype": "monetaryItemType"}, {"numeric_entity": "—", "datatype": "monetaryItemType"}, {"numeric_entity": "88", "datatype": "monetaryItemType"}, {"numeric_entity": "90", "datat... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>U.S. Plan U.S. Plan</td><td></td><td>Non-U.S. Plans</td></tr><tr><td></... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "—"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "94"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "96"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "—"
},
{
"datatype": "monetaryIte... |
138 | 138 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "898.6", "datatype": "monetaryItemType"}, {"numeric_entity": "878.2", "datatype": "monetaryItemType"}] | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Carrying Amount of the Hedged Liabi... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "898.6"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "878.2"
}
] |
139 | 139 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "23", "datatype": "monetaryItemType"}, {"numeric_entity": "29", "datatype": "monetaryItemType"}, {"numeric_entity": "69", "datatype": "monetaryItemType"}, {"numeric_entity": "71", "datatype": "monetaryItemType"}, {"numeric_entity": "2", "datatype": "monetaryItemType"}, {"numeric_entity": "3", "datat... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td>Current and Non-current Financing Receivables Current and Non-current Financing Receivables</td><td>As of</td></tr><tr><td></td><td>December 31, 2024</td><td>December 31, 2023</td... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "23"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "29"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "69"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "71"
},
{
"datatype": "monetaryI... |
152 | 152 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "197.9", "datatype": "monetaryItemType"}, {"numeric_entity": "40.2", "datatype": "monetaryItemType"}, {"numeric_entity": "37.5", "datatype": "monetaryItemType"}, {"numeric_entity": "—", "datatype": "monetaryItemType"}, {"numeric_entity": "26.5", "datatype": "monetaryItemType"}, {"numeric_entity": "3... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Total Federal</td... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "197.9"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "40.2"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "37.5"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "—"
},
{
"datatype": "mon... |
175 | 175 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "37818", "datatype": "sharesItemType"}, {"numeric_entity": "308", "datatype": "monetaryItemType"}, {"numeric_entity": "2957", "datatype": "monetaryItemType"}, {"numeric_entity": "663", "datatype": "monetaryItemType"}, {"numeric_entity": "3928", "datatype": "monetaryItemType"}, {"numeric_entity": "32... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Common stock shar... | [
{
"datatype": "sharesItemType",
"numeric_entity": "37818"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "308"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "2957"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "663"
},
{
"datatype": "mone... |
180 | 180 | train | <role>
You are an expert financial table tagging analyst.
</role>
<task>
Extract numeric entity and datatype pairs from the provided financial text/table.
Input: one financial text or HTML table.
Output: a JSON array. Each array item must contain exactly:
- "numeric_entity": the normalized numeric value string
- "dat... | [{"numeric_entity": "321.2", "datatype": "monetaryItemType"}, {"numeric_entity": "838.8", "datatype": "monetaryItemType"}, {"numeric_entity": "1686.3", "datatype": "monetaryItemType"}, {"numeric_entity": "2846.3", "datatype": "monetaryItemType"}, {"numeric_entity": "45.0", "datatype": "monetaryItemType"}, {"numeric_ent... | <table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Common Stock</td><td></td><td>Paid-in Capital</td><td... | [
{
"datatype": "monetaryItemType",
"numeric_entity": "321.2"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "838.8"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "1686.3"
},
{
"datatype": "monetaryItemType",
"numeric_entity": "2846.3"
},
{
"datatyp... |
End of preview. Expand in Data Studio
FinTagging Value-Type Extraction SFT Data
This dataset is derived from the sampled FinTagging 800/200 split. It defines an LLM instruction-following subtask where the model input is a prompt plus the financial table/text, and the model output is a JSON list of numeric entity/datatype pairs.
Task Format
query: full model input, including the instruction template and financial table/text.answer: JSON string target. It is a list of objects withnumeric_entityanddatatype.context: raw financial table/text without the instruction prompt.output_entities: structured copy ofanswerfor inspection.
Example answer:
[
{"numeric_entity": "62", "datatype": "monetaryItemType"},
{"numeric_entity": "171", "datatype": "monetaryItemType"}
]
Splits
| Split | Samples | Output entries | Unique datatypes |
|---|---|---|---|
| train | 800 | 11,687 | 5 |
| test | 200 | 2,546 | 5 |
Datatype Counts
| Datatype | Train count | Test count |
|---|---|---|
monetaryItemType |
10,448 | 2,285 |
percentItemType |
574 | 116 |
sharesItemType |
375 | 70 |
perShareItemType |
219 | 58 |
integerItemType |
71 | 17 |
Columns
source_sample_idx: original source row index.context_id: original context identifier.split: split label.query: prompt plus financial table/text.answer: JSON list target string.context: raw table/text.output_entities: structured target list.
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