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

This was partially offset by a $ .1 million increase in prepaid income taxes .The same store revenue increase is due primarily to a $ 2,012 , or 5.7 % , increase in average selling prices per unit , which increased rev The increase in net cash provided by financing activities was primarily due to a $ 346 million increase in net debt borrowings , partially o Depreciation expense increased $ 9.9 million primarily as a result of our recent acquisitions , including the black oil barge transportation Higher revenues were largely offset by a related increase in net fuel and purchased power costs of $ 20 million .The increase of $ 17.0 million in community operating expense from the Same Community Portfolio included a $ 5.8 million , or 2.1 % , increa Other noninterest income increased $ 14.6 million , or 109.7 percent , primarily driven by an $ 8.7 million adjustment decreasing the contin This increase was primarily a result of increases of $ 48.9 million in aggregate Affiliate expenses from the full year impact of new Affilia A 10 % increase in the Eurodollar rate would equal approximately five basis points .This change resulted in the portion of the rate increase attributed to net fuel costs being reduced , and the portion attributed to other no Ameren Illinois common stock dividend increased $ 194 million compared with 2010 .A $ 15 million increase in other labor costs , primarily because of staff additions due to the requirements of the IEIMA .We continue to lease approximately 32,900 square feet of administrative office and research and development space at our former corporate he

Nearby prompts. All 2,117 FIRE entities prompts

PromptCleanlab FIRE ·fire-0537

Other noninterest income increased $ 14.6 million , or 109.7 percent , primarily driven by an $ 8.7 million adjustment decreasing the contingent consideration liabilities on acquisitions .

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: none
Location: none
Money: $ 14.6 million; $ 8.7 million
Person: none
Product: none
Quantity: 109.7 percent

Ours

All 7 fields correct

```json
{
  "Company": None,
  "Date": None,
  "Location": None,
  "Money": ["$ 14.6 million", "$ 8.7 million"],
  "Person": None,
  "Product": None,
  "Quantity": ["109.7 percent"]
}
```

Aux 2015

Invalid JSON

{
  "Entity 1":

PiT-FT 2015

Invalid JSON

<|<>
<|<>
<|<>

The same 5-character fragment repeats 102 times until the token limit. Showing the first three.

ChronoGPT 2015

Invalid JSON

Entity 1: Company ⋆