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 combined 11.3 % reduction in expenses was in line with our 2012 business plan .Decreasing the long - term revenue growth rate by 0.5 % would decrease the equity value by approximately $ 3 million , or 7 % , using the in Similarly , a 100 basis point decrease in assumed interest rates would decrease annual interest expense by $ 1.4 million .A decrease in cash of $ 2.0 million due to an increase in other current assets primarily due to an increase in prepaid insurance and prepaid The variance was caused primarily from a reduction of $ 108,000 in fees and service charges .In response to the lower demand , we decreased our production volume by 4.5 million tons from 2011 to 2012 .The deferred tax valuation allowance was reduced by $ 0.2 million for our capital loss carryforwards , offset by a $ 0.1 million increase re First , our 2012 taxable operating income , exceeded the amount previously reflected in our deferred tax valuation model , resulting in a re For the year ended December 31 , 2011 , we had a net decrease of $ 824 million in borrowings due primarily to debt maturities within the yea In 2012 , the $ 35,000 decrease in appraisal fees resulted due to fewer appraisals paid for by the bank .Excluding the 2011 PropertyBridge divestiture ( See Note 3 Acquisitions and Disposals of the Notes to the Consolidated Financial Statements There was also a $ 21.2 million decrease in project development and selling costs , a $ 12.0 million decrease in professional service costs Offsetting this was a decrease of $ 3.5 million in the amortization of certain intangible assets primarily acquired via acquisitions in prio

Nearby prompts. All 2,117 FIRE entities prompts

PromptCleanlab FIRE ·fire-1441

The deferred tax valuation allowance was reduced by $ 0.2 million for our capital loss carryforwards , offset by a $ 0.1 million increase related to state and local net operating loss carryovers and foreign tax credits .

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: $ 0.1 million; $ 0.2 million
Person: none
Product: none
Quantity: none

Ours

Invalid JSON

[Could not generate without degeneration]

Aux 2015

Invalid JSON

{
  "Entity 1":

PiT-FT 2015

Invalid JSON

Empty response.

ChronoGPT 2015

Invalid JSON

Entity 1: Company �