Triple
T9578556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Training Day (TV series) |
E231109
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Jeff Cardoni |
E328813
|
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: Jeff Cardoni | Statement: [Training Day (TV series), composer, Jeff Cardoni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Cardoni Context triple: [Training Day (TV series), composer, Jeff Cardoni]
-
A.
Jeff Cardoni
chosen
Jeff Cardoni is an American film and television composer known for his versatile scores across comedies, dramas, and action projects.
-
B.
Joseph Cardone
Joseph Cardone is a character in the thriller film "The Osterman Weekend," involved in the web of espionage and deception surrounding a television host and his friends.
-
C.
Mike Piscitelli
Mike Piscitelli is a filmmaker and photographer known for his music videos, commercials, and visual collaborations with various artists.
-
D.
Steve Pellegrini
Steve Pellegrini is a Canadian municipal politician who serves as the long-time mayor of the Township of King in Ontario.
-
E.
Andy Robustelli
Andy Robustelli was a Hall of Fame defensive end best known for his dominant play with the New York Giants during the 1950s and early 1960s.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99aece1081908287e03106de020f |
completed | April 1, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1615d093c8190940037e9e0842db5 |
completed | April 4, 2026, 7:07 p.m. |
Created at: March 30, 2026, 8:05 p.m.