Triple
T16417045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G-Unit Records |
E398714
|
entity |
| Predicate | notableArtist |
P601
|
FINISHED |
| Object | Tony Yayo |
E603382
|
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: Tony Yayo | Statement: [G-Unit Records, notableArtist, Tony Yayo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Yayo Context triple: [G-Unit Records, notableArtist, Tony Yayo]
-
A.
Tony Yayo
chosen
Tony Yayo is an American rapper best known as a member of 50 Cent’s hip hop group G-Unit.
-
B.
Samson Kayo
Samson Kayo is a British actor and comedian known for his roles in television comedies such as "Timewasters," "Famalam," and "Bloods."
-
C.
Timaya
Timaya is a Nigerian singer and songwriter known for his influential role in contemporary Afrobeat and dancehall music.
-
D.
Tony Imi
Tony Imi was a British cinematographer known for his work on feature films and television, including the drama "International Velvet."
-
E.
Yung Gud
Yung Gud is a Swedish producer and DJ best known for his atmospheric, melancholic beats and frequent collaborations with rapper Yung Lean as part of the Sad Boys collective.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32877ff248190886717d3329421a7 |
completed | April 18, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00457f60c081908f4e46993780ac09 |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 5:09 a.m.