Triple
T23339400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiger Rag |
E591693
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Henry Ragas |
—
|
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: Henry Ragas | Statement: [Tiger Rag, composer, Henry Ragas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henry Ragas Context triple: [Tiger Rag, composer, Henry Ragas]
-
A.
Henry Ragas
chosen
Henry Ragas was an early jazz pianist best known for his work with the pioneering Original Dixieland Jass Band in the 1910s.
-
B.
Dan Harrow
Dan Harrow is the earnest, idealistic young farmer who serves as the central romantic lead in the stage musical and film "The Farmer Takes a Wife."
-
C.
Leo Feist
Leo Feist was an American music publisher and entrepreneur who became a prominent figure in the early 20th-century sheet music and popular song industry.
-
D.
Henry Ian Cusick
Henry Ian Cusick is a Scottish-Peruvian actor best known for his roles on television series such as Lost and The 100.
-
E.
James Huth
James Huth is a French film director, screenwriter, and producer best known for popular comedies such as the cult hit "Brice de Nice."
- 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f198317f4c8190a557cacb86568d6c |
completed | April 29, 2026, 5:33 a.m. |
Created at: April 17, 2026, 5:17 p.m.