Do No Harm
E1035485
Do No Harm is a work by Charlie Gogolak, likely a book or written piece exploring themes of ethics and responsibility.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Do No Harm canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T13334616 — 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: Do No Harm Context triple: [Charlie Gogolak, notableWork, Do No Harm]
-
A.
Safe from Harm
"Safe from Harm" is a 1990 trip-hop track by Massive Attack, known for its dark, atmospheric production and soulful vocals that helped define the group's early sound.
-
B.
No Bystanders
"No Bystanders" is a high-energy hip-hop track by Travis Scott from his album *Astroworld*, known for its mosh-pit-ready chorus and aggressive production.
-
C.
No Remorse
"No Remorse" is a song featured on the album "Adrenaline Rush."
-
D.
Harm of Will
"Harm of Will" is an experimental, atmospheric song by Björk from her 2001 album Vespertine, noted for its intricate production and intimate, ethereal vocals.
-
E.
No Good Deed
No Good Deed is a 2014 American thriller film starring Idris Elba and Taraji P. Henson, centered on a woman terrorized by an escaped convict who invades her home.
- 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: Do No Harm Target entity description: Do No Harm is a work by Charlie Gogolak, likely a book or written piece exploring themes of ethics and responsibility.
-
A.
Safe from Harm
"Safe from Harm" is a 1990 trip-hop track by Massive Attack, known for its dark, atmospheric production and soulful vocals that helped define the group's early sound.
-
B.
No Bystanders
"No Bystanders" is a high-energy hip-hop track by Travis Scott from his album *Astroworld*, known for its mosh-pit-ready chorus and aggressive production.
-
C.
No Remorse
"No Remorse" is a song featured on the album "Adrenaline Rush."
-
D.
Harm of Will
"Harm of Will" is an experimental, atmospheric song by Björk from her 2001 album Vespertine, noted for its intricate production and intimate, ethereal vocals.
-
E.
No Good Deed
No Good Deed is a 2014 American thriller film starring Idris Elba and Taraji P. Henson, centered on a woman terrorized by an escaped convict who invades her home.
- F. None of above. chosen
Statements (9)
| Predicate | Object |
|---|---|
| instanceOf | written work ⓘ |
| author | Charlie Gogolak ⓘ |
| creator | Charlie Gogolak ⓘ |
| hasSubject |
ethical decision-making
ⓘ
moral responsibility ⓘ |
| language | English ⓘ |
| mainTheme |
ethics
ⓘ
responsibility ⓘ |
| title | Do No Harm ⓘ |
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: Do No Harm Description of subject: Do No Harm is a work by Charlie Gogolak, likely a book or written piece exploring themes of ethics and responsibility.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.