Here we tabulate UN Human Rights document:

tblHR=LLMResourceFunction["ThemeTableJSON"][ExampleData[{"Text","UNHumanRightsEnglish"}]]

Here we use the second argument to specify that the text is a theatrical play and the content descriptions are no more than 20 words:

tblHamlet=LLMResourceFunction["ThemeTableJSON"][ExampleData[{"Text","Hamlet"}],"theatrical play",20]

Scope (3)

The following function converts wider range of LLM results obtained by requests to generate JSON code:

Clear[FromJSON];
FromJSON[t_String]:=ImportString[FromCharacterCode@ToCharacterCode[StringReplace[t,{StartOfString~~"```json","```"~~EndOfString}->""],"UTF-8"],"RawJSON"];

Here we convert the LLM Human Rights result into a table:

ResourceFunction["GridTableForm"]@Dataset[tblHR//FromJSON]

Here we convert the "Hamlet" LLM result into a table:

ResourceFunction["GridTableForm"]@Dataset[tblHamlet//FromJSON]

Possible Issues

For most articles only models with larger limits of input tokens can applied. For example, (current) OpenAI's ChatGPT model "gpt-4-turbo-preview" that allows 128K input tokens.