Triple
T22663364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R. D. Burman |
E559718
|
entity |
| Predicate | numberOfFilmsScoredApprox |
P30144
|
FINISHED |
| Object | 300 |
—
|
LITERAL 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: 300 | Statement: [R. D. Burman, numberOfFilmsScoredApprox, 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilmsScoredApprox Context triple: [R. D. Burman, numberOfFilmsScoredApprox, 300]
-
A.
hasFilmScore
Indicates that one entity serves as the musical score or soundtrack composed for a particular film.
-
B.
hasFilmScoreBy
Indicates that a film’s musical score was composed or created by a specified person or entity.
-
C.
numberOfFilmScores
chosen
Indicates the total count of film scores that an entity has composed or is credited with.
-
D.
typicalNumberOfSelectedFilms
Indicates the usual or average number of films that are chosen or selected in a given context or process.
-
E.
numberOfFilmsCovered
Indicates the total count of films that are included, addressed, or handled within a given context or entity.
- F. None of above.
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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f176617ed8819095a58a2c9f1e3918 |
completed | April 29, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69ee62a6245881909506ff502da14137 |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:08 p.m.