Triple
T13882519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jon Bauman |
E333752
|
entity |
| Predicate | stageName |
P7872
|
FINISHED |
| Object | Bowzer |
E1068591
|
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: Bowzer | Statement: [Jon Bauman, stageName, Bowzer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bowzer Context triple: [Jon Bauman, stageName, Bowzer]
-
A.
Bowzer
chosen
Bowzer is the stage name of Jon Bauman, an American musician and television personality best known as the charismatic, greaser-style frontman of the retro rock-and-roll group Sha Na Na.
-
B.
Slippy Toad
Slippy Toad is a mechanically gifted but often comical amphibian pilot and member of the Star Fox team in Nintendo’s Star Fox video game series.
-
C.
Ryan Bowser
Ryan Bowser is an American music producer best known for his work in R&B and hip-hop, including crafting hits for artists like Nelly and Kelly Rowland.
-
D.
Mario Banana
Mario Banana is a film featuring the underground drag performer and Warhol superstar Mario Montez.
-
E.
Mário
Mário is a masculine given name of Latin origin, widely used in Portuguese- and Italian-speaking countries.
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0bea4d248190bcbcea9ea875c5f9 |
completed | April 14, 2026, 9:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce6facd48190b310099fbd52bdf0 |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 10:15 p.m.