Triple
T956797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Narcos |
E20641
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Carlo Bernard |
E149268
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Carlo Bernard | Statement: [Narcos, executiveProducer, Carlo Bernard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carlo Bernard Context triple: [Narcos, executiveProducer, Carlo Bernard]
-
A.
Carlo Bernard
chosen
Carlo Bernard is an American screenwriter and producer best known for co-creating the acclaimed crime drama television series "Narcos."
-
B.
Harold McLernon
Harold McLernon was a film editor known for his work on early sound-era movies, including the 1928 musical drama "The Singing Fool."
-
C.
Clifton Daniel
Clifton Daniel was an American newspaper editor and managing editor of The New York Times, known also as the son-in-law of U.S. President Harry S. Truman.
-
D.
Hugh Garner
Hugh Garner was a Canadian author best known for his socially conscious novels and short stories depicting working-class life in Toronto.
-
E.
Charles Bickford
Charles Bickford was an American character actor known for his rugged screen presence and acclaimed supporting roles in numerous classic Hollywood films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a493b21f2881908132dcf45dcd2f36 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3f981bc819098125554eeeb6375 |
completed | March 1, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbad10ae48190af7dcaa8dfff30ec |
completed | March 7, 2026, 11:54 p.m. |
Created at: March 1, 2026, 7:40 p.m.