Triple
T3564545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Training Day |
E75412
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Mark Mancina |
E354225
|
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: Mark Mancina | Statement: [Training Day, musicBy, Mark Mancina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Mancina Context triple: [Training Day, musicBy, Mark Mancina]
-
A.
Mark Mancina
chosen
Mark Mancina is an American composer best known for his work on film scores and soundtracks across action, animation, and adventure movies.
-
B.
Michael Kamen
Michael Kamen was an American composer and conductor renowned for his film and television scores, including major works in action cinema and acclaimed historical dramas.
-
C.
Christophe Beck
Christophe Beck is a Canadian composer best known for his film and television scores, including work on projects like "Buffy the Vampire Slayer" and various major Hollywood films.
-
D.
Ron Goodwin
Ron Goodwin was a British composer and conductor best known for his rousing film scores for war and adventure movies in the mid-20th century.
-
E.
Albert Weinert
Albert Weinert was a German-American sculptor and monument designer known for his public memorials in the United States.
- 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_69ad85d512708190829c8b2d3a2ccfb8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0a76c008190a2056b6d990776fe |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bba91d08819081a433c9f06a2c2b |
completed | March 13, 2026, 7:24 a.m. |
Created at: March 8, 2026, 3:21 p.m.