Triple
T16504859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yung Joc |
E400899
|
entity |
| Predicate | memberOf |
P10
|
FINISHED |
| Object | Bad Boy South |
E1156163
|
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: Bad Boy South | Statement: [Yung Joc, memberOf, Bad Boy South]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Boy South Context triple: [Yung Joc, memberOf, Bad Boy South]
-
A.
Bad Boy South
chosen
Bad Boy South is a Southern hip hop-focused imprint of Bad Boy Records known for signing and promoting artists from the Southern United States rap scene.
-
B.
Bad Boy Hitmen
Bad Boy Hitmen is a production team associated with Bad Boy Records, known for crafting many of the label’s signature hip-hop and R&B hits in the 1990s and early 2000s.
-
C.
Bad Boy
Bad Boy is a 1949 American crime drama film about a troubled juvenile delinquent given a final chance at reform.
-
D.
American Bad Boy
American Bad Boy is an independent urban drama film featuring Torrei Hart in a prominent role.
-
E.
Bad Boy Timz
Bad Boy Timz is a Nigerian singer and songwriter known for his Afrobeat and Afropop hits and energetic, melodic style.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e51ce1c81909548298f703a7ffa |
completed | April 18, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0058305e308190a22cbd03daec53aa |
completed | May 10, 2026, 10:04 a.m. |
Created at: April 10, 2026, 5:14 a.m.