Triple
T21736265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Traces of Red |
E536531
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | David Michael Frank |
—
|
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: David Michael Frank | Statement: [Traces of Red, musicBy, David Michael Frank]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Michael Frank Context triple: [Traces of Red, musicBy, David Michael Frank]
-
A.
David Michael Frank
chosen
David Michael Frank is an American composer best known for his work on film and television scores.
-
B.
Don Michael Paul
Don Michael Paul is an American filmmaker and former actor known for directing numerous direct-to-video action and genre films.
-
C.
David Frank
David Frank is a music producer best known for his work on Christina Aguilera’s hit single "Genie in a Bottle."
-
D.
Michael Raffetto
Michael Raffetto was an American radio actor best known for his prominent roles in classic radio dramas during the 1930s and 1940s.
-
E.
Michael Fink
Michael Fink is a fashion designer known for his work in high-end apparel and creative direction within the fashion industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69effd0c0a088190bd1926fa4b73d8f4 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 16, 2026, 6:49 p.m.