Rachel Maddow Presents: Déjà News
E634552
"Rachel Maddow Presents: Déjà News" is a narrative podcast series in which Rachel Maddow revisits historical events to illuminate their parallels with contemporary American politics and news.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Rachel Maddow Presents: Déjà News canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T6999347 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Maddow Presents: Déjà News Context triple: [Rachel Maddow, notableWork, Rachel Maddow Presents: Déjà News]
-
A.
The Rachel Maddow Show
The Rachel Maddow Show is a primetime American news and opinion television program hosted by political commentator Rachel Maddow, known for its in-depth analysis of U.S. politics and current events.
-
B.
A Current Affair
A Current Affair is a long-running Australian television current affairs program known for its tabloid-style coverage of news, human-interest stories, and consumer issues.
-
C.
Fox News Sunday
Fox News Sunday is a weekly American television news and political talk show featuring interviews with key policymakers and analysis of current events.
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D.
The MacNeil/Lehrer Report
The MacNeil/Lehrer Report was a pioneering American public television news program known for its in-depth, sober analysis of national and international events, which later evolved into what is now PBS NewsHour.
-
E.
Rachel Maddow
Rachel Maddow is an American television news host, political commentator, and author best known for anchoring "The Rachel Maddow Show" on MSNBC.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rachel Maddow Presents: Déjà News Target entity description: "Rachel Maddow Presents: Déjà News" is a narrative podcast series in which Rachel Maddow revisits historical events to illuminate their parallels with contemporary American politics and news.
-
A.
The Rachel Maddow Show
The Rachel Maddow Show is a primetime American news and opinion television program hosted by political commentator Rachel Maddow, known for its in-depth analysis of U.S. politics and current events.
-
B.
A Current Affair
A Current Affair is a long-running Australian television current affairs program known for its tabloid-style coverage of news, human-interest stories, and consumer issues.
-
C.
Fox News Sunday
Fox News Sunday is a weekly American television news and political talk show featuring interviews with key policymakers and analysis of current events.
-
D.
The MacNeil/Lehrer Report
The MacNeil/Lehrer Report was a pioneering American public television news program known for its in-depth, sober analysis of national and international events, which later evolved into what is now PBS NewsHour.
-
E.
Rachel Maddow
Rachel Maddow is an American television news host, political commentator, and author best known for anchoring "The Rachel Maddow Show" on MSNBC.
- F. None of above. chosen
Statements (38)
| Predicate | Object |
|---|---|
| instanceOf |
non-fiction podcast
ⓘ
podcast series ⓘ |
| aim |
to connect past events with current politics
ⓘ
to provide historical context for modern news ⓘ |
| basedOn | historical events ⓘ |
| countryOfOrigin |
United States of America
ⓘ
surface form:
United States
|
| distributionPlatform | podcast apps ⓘ |
| factuality | non-fiction ⓘ |
| features |
analysis of contemporary American news
ⓘ
in-depth historical research ⓘ |
| format | audio podcast ⓘ |
| genre |
history podcast
ⓘ
news analysis podcast ⓘ politics podcast ⓘ |
| hasPart | episodes ⓘ |
| host | Rachel Maddow NERFINISHED ⓘ |
| hostNotableFor | The Rachel Maddow Show NERFINISHED ⓘ |
| hostProfession |
political commentator
ⓘ
television news anchor ⓘ |
| intendedEffect | to help listeners understand current events through history ⓘ |
| language | English ⓘ |
| mediaType | digital audio ⓘ |
| narrativeFocus | parallels between history and current events ⓘ |
| narrativePerspective | American political perspective ⓘ |
| narrativeStyle | storytelling ⓘ |
| presenter | Rachel Maddow NERFINISHED ⓘ |
| primaryAudience |
listeners interested in U.S. politics
ⓘ
listeners interested in history ⓘ |
| productionType | scripted podcast ⓘ |
| subjectMatter |
American politics
ⓘ
United States history ⓘ contemporary news ⓘ |
| topic |
democracy in the United States
ⓘ
historical precedents for modern issues ⓘ media and politics ⓘ political crises ⓘ |
| typicalEpisodeContent |
comparison to a current political or news situation
ⓘ
retelling of a historical event ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Rachel Maddow Presents: Déjà News Description of subject: "Rachel Maddow Presents: Déjà News" is a narrative podcast series in which Rachel Maddow revisits historical events to illuminate their parallels with contemporary American politics and news.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.