Triple
T16810570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Nanny |
E408596
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | David Michael Frank |
E570793
|
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: David Michael Frank | Statement: [Mr. Nanny, musicBy, David Michael Frank]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Michael Frank Context triple: [Mr. Nanny, 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.
David Frank
David Frank is a music producer best known for his work on Christina Aguilera’s hit single "Genie in a Bottle."
-
C.
Michael Raffetto
Michael Raffetto was an American radio actor best known for his prominent roles in classic radio dramas during the 1930s and 1940s.
-
D.
Michael Fink
Michael Fink is a fashion designer known for his work in high-end apparel and creative direction within the fashion industry.
-
E.
Michael James Gubitosi
Michael James Gubitosi is the birth name of American actor Robert Blake, known for his roles in the film "In Cold Blood" and the TV series "Baretta."
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2cf680c8190bcd640570c524918 |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b290d8e4819082880444b42ffa43 |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.