Triple
T35922125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1944 Academy Awards |
E1038913
|
entity |
| Predicate | bestScoringOfAMusicalPictureComposer |
P184463
|
FINISHED |
| Object | Ray Heindorf |
—
|
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: Ray Heindorf | Statement: [1944 Academy Awards, bestScoringOfAMusicalPictureComposer, Ray Heindorf]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestScoringOfAMusicalPictureComposer Context triple: [1944 Academy Awards, bestScoringOfAMusicalPictureComposer, Ray Heindorf]
-
A.
bestScoringOfAMusicalPictureWinner
Indicates that the subject is the highest-scoring entity among those that have won the Best Musical Picture award.
-
B.
oscarNomineeComposer
Indicates that a person is a film composer who has been nominated for an Academy Award (Oscar) for their work in film music.
-
C.
directorAlsoComposedMusic
Indicates that the person who directed the work also composed its musical score.
-
D.
scoredBySameComposerAsFilm
Indicates that the work is scored by a composer who also composed the score for the referenced film.
-
E.
academyAwardForBestMusicScoringOfADramaticOrComedyPicture
Indicates that an entity received the Academy Award for Best Music Scoring of a Dramatic or Comedy Picture for a particular film.
- F. None of above. chosen
Provenance (4 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.