Triple
T12990941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mama Africa |
E321903
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mr. Chidoo |
E978171
|
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: Mr. Chidoo | Statement: [Mama Africa, producer, Mr. Chidoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Chidoo Context triple: [Mama Africa, producer, Mr. Chidoo]
-
A.
Mr Chidoo
chosen
Mr Chidoo is a Nigerian music producer best known for his work on Davido’s breakout album "Omo Baba Olowo."
-
B.
Mr Chido
Mr Chido is a music producer best known for his work on the television sitcom "The King of Queens."
-
C.
Chee Dale
Chee Dale is a steep, wooded limestone gorge in the Peak District of Derbyshire, England, known for its dramatic cliffs, riverside walking paths, and rock climbing routes.
-
D.
Señor Chang
Señor Chang is the unhinged, often antagonistic Spanish teacher-turned-student from the TV series "Community," known for his erratic behavior and over-the-top antics.
-
E.
Chee-Chee
Chee-Chee is the loyal and intelligent monkey companion of Doctor Dolittle in Hugh Lofting’s classic children’s book series.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7765788190a9503ef055bc30ca |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0fca5e4819086b010fdd1813419 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:43 p.m.