Triple
T4441041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kindergarten Cop |
E95770
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Randy Edelman |
E359056
|
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: Randy Edelman | Statement: [Kindergarten Cop, musicBy, Randy Edelman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Randy Edelman Context triple: [Kindergarten Cop, musicBy, Randy Edelman]
-
A.
Randy Edelman
chosen
Randy Edelman is an American composer best known for his prolific work on film and television scores, including numerous Hollywood action and drama movies.
-
B.
Albert Weinert
Albert Weinert was a German-American sculptor and monument designer known for his public memorials in the United States.
-
C.
Dave Grusin
Dave Grusin is an American composer, arranger, and jazz pianist best known for his prolific film and television scores and for co-founding GRP Records.
-
D.
Ron Goodwin
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.
-
E.
Harold Faltermeyer
Harold Faltermeyer is a German composer, keyboardist, and producer best known for his iconic 1980s film scores and synth-driven themes such as "Axel F."
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355ad71588190b1dcad4250472c29 |
completed | March 13, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b61380fca08190bf036a7d82cee0e7 |
completed | March 15, 2026, 2:03 a.m. |
Created at: March 12, 2026, 11:32 p.m.