Triple
T14397809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Can't Stop the Music |
E356994
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Bill Butler |
E33313
|
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: Bill Butler | Statement: [Can't Stop the Music, cinematographyBy, Bill Butler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Butler Context triple: [Can't Stop the Music, cinematographyBy, Bill Butler]
-
A.
Bill Butler
chosen
Bill Butler was an acclaimed American cinematographer best known for his influential work on landmark films of the 1970s and 1980s.
-
B.
Steve Butler
Steve Butler is a fictional character portrayed by Tommy Lee Jones in the film "Heaven & Earth."
-
C.
Reg Butler
Reg Butler was a British sculptor known for his expressive, often skeletal metal figures and his prominence in postwar European modernist sculpture.
-
D.
Joe Butler
Joe Butler is a film editor best known for his work on the animated feature "Ron’s Gone Wrong."
-
E.
John K. Butler
John K. Butler was an American screenwriter best known for his work on mid-20th-century Westerns and crime films.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90826f908190b3969af9b7cf922f |
completed | April 14, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd551cbdb08190a9ea53e607f2555b |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:17 a.m.