Triple
T2830437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rio |
E62222
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | John Powell |
E74988
|
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: John Powell | Statement: [Rio, musicBy, John Powell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Powell Context triple: [Rio, musicBy, John Powell]
-
A.
John Powell
chosen
John Powell is a British-born, Academy Award–nominated film composer renowned for his dynamic scores for animated and action films such as the "How to Train Your Dragon" series and the "Bourne" franchise.
-
B.
John Debney
John Debney is an American film composer known for scoring a wide range of movies and television shows, including major studio productions and acclaimed dramas.
-
C.
James Newton Howard
James Newton Howard is an acclaimed American composer best known for his prolific film and television scores across a wide range of genres.
-
D.
Michael Abels
Michael Abels is an American composer best known for his innovative, genre-blending film scores for Jordan Peele’s movies, including Get Out, Us, and Nope.
-
E.
Steve Jablonsky
Steve Jablonsky is an American composer best known for his film and television scores, particularly for the Transformers franchise and numerous action and science-fiction projects.
- 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdebd5a2c81908f0e30a0ae0eb8df |
completed | March 7, 2026, 8:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3544e32a0819087d554982f443b1d |
completed | March 13, 2026, 12:03 a.m. |
Created at: March 6, 2026, 10:01 p.m.