Output Explorer

Every prompt in the paper, and what each model wrote back.

Extract seven entity types from one sentence of financial news as JSON. Scored per field against the Cleanlab reference.

13 of 2,117 prompts

A net $ 2.8 million decrease in amortization of other acquired software , including Esterel .Consistent with our improved financial performance , higher incentive compensation expense , along with increased information technology cos Partially offsetting these increases was a reduction in bonus expense of $ 1.8 million .The PSP Note included embedded interest rate floors and deferred financing costs which , as of March 2012 , was a decrease to the PSP Note o The agreement contains a covenant that requires our ratio of total debt to total capitalization not to exceed 65 percent as of the last day As of December 31 , 2012 , the Company had accrued approximately $ 64.4 million for pending copyright fee issues , including litigation proc The 34,134 shares acquired in the three months ended December 31 , 2012 represent Class A common stock acquired by us from our employees who As of December 31 , 2012 , we have accumulated net losses of $ 127.4 million .Investing activities The $ 197.4 million increase in net cash flows used for investing activities during 2012 compared to that of 2011 was d Undistributed earnings of the Company s foreign subsidiary amounted to approximately $ 15.8 million as of December 31 , 2012 .16 Table of Contents Reported sales increased 12 percent in 2011 compared with 2010 .The most recent milestone payments under these agreements were made during 2011 , when we received $ 25 million from Daiichi Sankyo resultin In 2013 , we expect to reclassify another $ 41.3 million of long - term deferred income tax liabilities to current deferred income taxes .

Nearby prompts. All 2,117 FIRE entities prompts

PromptCleanlab FIRE ·fire-0508

The 34,134 shares acquired in the three months ended December 31 , 2012 represent Class A common stock acquired by us from our employees who surrendered shares in lieu of cash either to fund their exercise of stock options or to pay taxes on equity awards made under our Second Amended and Restated 2004 Omnibus Incentive Plan .

Extraction instructions · system prompt, 2,489 characters, identical for every model

Identify and extract entities from the following financial news text into the following categories:

Entity 1: Company 
⋆ Definition: Denotes the official or unofficial name of a registered company or a brand.
⋆ Example entities: {Apple Inc.; Uber; Bank of America}

Entity 2: Date 
⋆ Definition: Represents a specific time period, whether explicitly mentioned (e.g., "year ended March 2020") or implicitly referred to (e.g., "last month"), in the past, present, or future.
⋆ Example entities: {June 2nd, 2010; quarter ended 2021; last week; prior year; Wednesday}

Entity 3: Location 
⋆ Definition: Represents geographical locations, such as political regions, countries, states, cities, roads, or any other location, even when used as adjectives.
⋆ Example entities: {California; Paris; 1280 W 12th Blvd; Americas; Europe}

Entity 4: Money 
⋆ Definition: Denotes a monetary value expressed in any world currency, including digital currencies.
⋆ Example entities: {$76.3 million; $4 Bn; Rs 33.80 crore; 1.2 BTC}

Entity 5: Person 
⋆ Definition: Represents the name of an individual.
⋆ Example entities: {Meg Whitman; Mr. Baker; Warren Buffet}

Entity 6: Product 
⋆ Definition: Refers to any physical object or service manufactured or provided by a company to consumers, excluding references to businesses or sectors within the financial context.
⋆ Example entities: {iPhone; Tesla model X; cloud services; Microsoft Windows 10; laptops; medical equipment; computer software; online classes; eye surgery}

Entity 7: Quantity 
⋆ Definition: Represents any numeric value that is not categorized as Money, such as percentages, numbers, measurements (e.g., weight, length), or other similar quantities. Note that unit of measurements are also part of the entity.
⋆ Example entities: {15%; 25,000 units; 2.75in; 100 tons}

For each category:
- Extract all relevant entities as a list of strings, preserving the wording from the text
- Use None if no entities are found in that category
- Only extract entities that are explicitly mentioned in the text itself, do not make inferences or reason about what entities might be implied based on URLs, domain names, or other indirect references
- Extract individual items rather than compound or ranged entities (e.g., if a range or compound entity is mentioned, extract each individual item separately)

Return the extracted information as a JSON object with all categories included, using None for cases where no entities are found.

Expected answer

Company: none
Date: 2004; three months ended December 31 , 2012
Location: none
Money: none
Person: none
Product: none
Quantity: 34,134

Ours

6 of 7 fields correct

```json
{
  "Company": None,
  "Date": [
    "three months ended December 31 , 2012",
    "Second Amended and Restated 2004 Omnibus Incentive Plan"
  ],
  "Location": None,
  "Money": None,
  "Person": None,
  "Product": [
    "stock options",
    "Class A common stock"
  ],
  "Quantity": [
    "34,134 shares"
  ]
}
```

Aux 2015

Invalid JSON

{
  "Company": {

PiT-FT 2015

Invalid JSON

Empty response.

ChronoGPT 2015

Invalid JSON