Triple
T20632752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leo Feist |
E506998
|
entity |
| Predicate | knownAs |
P39
|
FINISHED |
| Object | Leo Feist |
—
|
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: Leo Feist | Statement: [Leo Feist, knownAs, Leo Feist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leo Feist Context triple: [Leo Feist, knownAs, Leo Feist]
-
A.
Leo Feist
chosen
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.
-
B.
Henry Ragas
Henry Ragas was an early jazz pianist best known for his work with the pioneering Original Dixieland Jass Band in the 1910s.
-
C.
Christopher Hesse
Christopher Hesse is a computer scientist and machine learning researcher known for his work at OpenAI, including co-authoring influential papers on large language models.
-
D.
Daniel Bachman
Daniel Bachman is an American guitarist and composer known for his intricate, atmospheric solo acoustic work in the contemporary folk and American primitive traditions.
-
E.
Michael Wandmacher
Michael Wandmacher is an American film and television composer known for his work on horror and action projects, including the score for "My Bloody Valentine 3D."
- 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_69e0b4bd4a0081908d4e97a590a33fb2 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad0bdcd88190a59d68e03370b271 |
completed | April 20, 2026, 10:47 p.m. |
Created at: April 16, 2026, 11:42 a.m.