Triple
T12119626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cavalier Johnson |
E288656
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Cavalier Johnson |
E57505
|
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: Cavalier Johnson | Statement: [Cavalier Johnson, name, Cavalier Johnson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cavalier Johnson Context triple: [Cavalier Johnson, name, Cavalier Johnson]
-
A.
Cavalier Johnson
chosen
Cavalier Johnson is an American politician who serves as the mayor of Milwaukee, Wisconsin.
-
B.
Reggie Johnson
Reggie Johnson is an American jazz double bassist known for his work in the hard bop and post-bop scenes from the 1960s onward.
-
C.
Marcell Johnson
Marcell Johnson is the son of acclaimed American actress Taraji P. Henson and has occasionally appeared in film and television projects.
-
D.
Joe Johnson
Joe Johnson is a music producer known for his work on the song "Hello Mary Lou."
-
E.
Cedric Yarbrough
Cedric Yarbrough is an American actor and comedian best known for his roles on the TV series "Reno 911!" and for voicing characters in animated shows such as "The Boondocks."
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91577a03c81909add7a5d7324a648 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f682397c819085a86a98e079660b |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:49 p.m.