Triple
T20177867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viva La Bam |
E492647
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Rake Yohn
Rake Yohn is an American television personality and member of the CKY crew best known for his appearances alongside Bam Margera on MTV reality shows.
|
E1416707
|
NE FINISHED |
How this triple was built (4 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: Rake Yohn | Statement: [Viva La Bam, starring, Rake Yohn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rake Yohn Context triple: [Viva La Bam, starring, Rake Yohn]
-
A.
Yosho
Yosho is a key character in the Tenchi Muyo! franchise, known as Tenchi Masaki’s grandfather and a powerful Juraian prince and swordsman.
-
B.
Naoyoshi
Naoyoshi is a Japanese given name commonly used for males.
-
C.
Yorii
Yorii is a town in Saitama Prefecture, Japan, known as a regional residential and commuter hub connected to the greater Tokyo area.
-
D.
Yoshio
Yoshio is a Japanese masculine given name that can be written with various kanji characters and is borne by numerous real and fictional individuals.
-
E.
Ryogo
Ryogo is a Japanese given name most notably borne by theoretical physicist Ryogo Kubo, known for his contributions to statistical mechanics and the fluctuation-dissipation theorem.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rake Yohn Triple: [Viva La Bam, starring, Rake Yohn]
Generated description
Rake Yohn is an American television personality and member of the CKY crew best known for his appearances alongside Bam Margera on MTV reality shows.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rake Yohn Target entity description: Rake Yohn is an American television personality and member of the CKY crew best known for his appearances alongside Bam Margera on MTV reality shows.
-
A.
Yosho
Yosho is a key character in the Tenchi Muyo! franchise, known as Tenchi Masaki’s grandfather and a powerful Juraian prince and swordsman.
-
B.
Naoyoshi
Naoyoshi is a Japanese given name commonly used for males.
-
C.
Yorii
Yorii is a town in Saitama Prefecture, Japan, known as a regional residential and commuter hub connected to the greater Tokyo area.
-
D.
Yoshio
Yoshio is a Japanese masculine given name that can be written with various kanji characters and is borne by numerous real and fictional individuals.
-
E.
Ryogo
Ryogo is a Japanese given name most notably borne by theoretical physicist Ryogo Kubo, known for his contributions to statistical mechanics and the fluctuation-dissipation theorem.
- F. None of above. chosen
Provenance (5 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e668ec4d7c81909fa4bdc58ed54aeb |
completed | April 20, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a083c79d2948190a1df0d3bd1f9c6ad |
completed | May 16, 2026, 9:44 a.m. |
| NEDg | Description generation | batch_6a083d86085c8190a71d0dce65c659a4 |
completed | May 16, 2026, 9:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a083eb380f48190bda8549d122f830b |
completed | May 16, 2026, 9:53 a.m. |
Created at: April 11, 2026, 11:36 p.m.