Triple
T3321500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Force 10 from Navarone |
E69804
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Ron Goodwin |
E348324
|
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: Ron Goodwin | Statement: [Force 10 from Navarone, musicBy, Ron Goodwin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ron Goodwin Context triple: [Force 10 from Navarone, musicBy, Ron Goodwin]
-
A.
Ron Goodwin
chosen
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.
-
B.
Albert Weinert
Albert Weinert was a German-American sculptor and monument designer known for his public memorials in the United States.
-
C.
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.
-
D.
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.
-
E.
John Powell
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.
- 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_69ad85a1829881908942c14075644d0d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb13b85208190b13aba355d5dafcf |
completed | March 8, 2026, 5:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3250c720c81908a8a6fed9a6fd349 |
completed | March 12, 2026, 8:41 p.m. |
Created at: March 8, 2026, 3:11 p.m.