Triple
T21097446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Layzie Bone |
E519803
|
entity |
| Predicate | hasStageName |
P7872
|
FINISHED |
| Object | Layzie Bone |
—
|
NE NERFINISHED |
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: Layzie Bone | Statement: [Layzie Bone, hasStageName, Layzie Bone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Layzie Bone Context triple: [Layzie Bone, hasStageName, Layzie Bone]
-
A.
Layzie Bone
chosen
Layzie Bone is an American rapper best known as a member of the Grammy-winning hip hop group Bone Thugs-n-Harmony.
-
B.
Krayzie Bone
Krayzie Bone is an American rapper, singer, and member of the Grammy-winning hip hop group Bone Thugs-n-Harmony, known for his rapid-fire delivery and melodic style.
-
C.
Bizzy Bone
Bizzy Bone is an American rapper best known as a member of the Grammy-winning hip hop group Bone Thugs-n-Harmony, recognized for his rapid-fire delivery and melodic style.
-
D.
Killa Bounce
Killa Bounce is a track featured on the album "Purple Haze 2" by rapper Cam'ron.
-
E.
Lord Finesse
Lord Finesse is an American hip-hop MC and producer from the Bronx, best known as a founding member of the Diggin' in the Crates Crew and for his influential work in 1990s East Coast rap.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b508d8dc81909be940dafe36c8f7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e71b5a3a9481908e30fba9717dc461 |
completed | April 21, 2026, 6:38 a.m. |
Created at: April 16, 2026, 2:52 p.m.