Triple
T23321149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How I Could Just Kill a Man |
E591152
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Sen Dog |
—
|
NE NERFINISHED |
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: Sen Dog | Statement: [How I Could Just Kill a Man, writer, Sen Dog]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sen Dog Context triple: [How I Could Just Kill a Man, writer, Sen Dog]
-
A.
Sen Dog
chosen
Sen Dog is a Cuban-American rapper best known as a founding member and hype man of the pioneering hip hop group Cypress Hill.
-
B.
Perros
Perros is a film project featuring Mexican actor Jorge Antonio Guerrero, known for his roles in acclaimed Latin American cinema.
-
C.
Mr. Dog
Mr. Dog is a musical project or band associated with musician David Bryson, known for his work in alternative rock.
-
D.
O-Dog
O-Dog is a volatile, trigger-happy young gang member from the film "Menace II Society," known for his ruthless violence and charismatic recklessness.
-
E.
Doggie
Doggie is a nickname of Tony Pérez, the Hall of Fame Cuban-American first baseman best known for his years with the Cincinnati Reds' "Big Red Machine."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d1effe4819096907f95f610dbff |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19785ae5481908816b37da95ceb3e |
completed | April 29, 2026, 5:30 a.m. |
Created at: April 17, 2026, 5:07 p.m.