Triple
T16438576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hot Stuff |
E399237
|
entity |
| Predicate | songwriter |
P1141
|
FINISHED |
| Object | Harold Faltermeyer |
E341021
|
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: Harold Faltermeyer | Statement: [Hot Stuff, songwriter, Harold Faltermeyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harold Faltermeyer Context triple: [Hot Stuff, songwriter, Harold Faltermeyer]
-
A.
Harold Faltermeyer
chosen
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."
-
B.
Don Grusin
Don Grusin is an American jazz and fusion keyboardist, composer, and producer known for his solo work and collaborations within contemporary jazz, including projects with his brother Dave Grusin.
-
C.
Albert Weinert
Albert Weinert was a German-American sculptor and monument designer known for his public memorials in the United States.
-
D.
Bill Conti
Bill Conti is an American composer and conductor best known for his iconic film and television scores, including the music for the Rocky series and various popular TV shows.
-
E.
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.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32ba5a4748190b63ff53bfb5957c7 |
completed | April 18, 2026, 6:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0058143cb88190943b951cc8e47a66 |
completed | May 10, 2026, 10:04 a.m. |
Created at: April 10, 2026, 5:10 a.m.