Triple
T14559667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One in a Million |
E341634
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Vincent Herbert |
E826617
|
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: Vincent Herbert | Statement: [One in a Million, producer, Vincent Herbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vincent Herbert Context triple: [One in a Million, producer, Vincent Herbert]
-
A.
Vincent Herbert
chosen
Vincent Herbert is an American music producer and songwriter best known for his work with major R&B and pop artists and for founding Streamline Records.
-
B.
Vincent Brooks
Vincent Brooks is a retired U.S. Army general best known for his prominent leadership roles during the Iraq War and his later command of U.S. Forces Korea.
-
C.
Robert Vernay
Robert Vernay was a French film director best known for his mid-20th-century adaptations of classic literary works.
-
D.
Vincent Gray
Vincent Gray is an American politician who served as the mayor of Washington, D.C., from 2011 to 2015.
-
E.
Victor Arnold
Victor Arnold was an American character actor known for his tough, streetwise roles in crime and action films and television from the 1960s through the 1990s.
- 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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3881b788190922932fb8ff81160 |
completed | April 14, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda9124af481908f43e3b541568e26 |
completed | May 8, 2026, 9:12 a.m. |
Created at: April 10, 2026, 1:23 a.m.