Triple
T22786393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mission: Impossible theme |
E563978
|
entity |
| Predicate | arrangedBy |
P4873
|
FINISHED |
| Object | Joe Kraemer |
—
|
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: Joe Kraemer | Statement: [Mission: Impossible theme, arrangedBy, Joe Kraemer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Kraemer Context triple: [Mission: Impossible theme, arrangedBy, Joe Kraemer]
-
A.
Joe Kraemer
chosen
Joe Kraemer is an American film composer best known for his scores for action and thriller films, including several collaborations with director Christopher McQuarrie.
-
B.
Jeff Kramer
Jeff Kramer is a prominent computer scientist recognized for his influential contributions to software engineering research.
-
C.
Phil Fehrle
Phil Fehrle is a film producer best known for his work on the family fantasy movie "Thomas and the Magic Railroad."
-
D.
Jim Krueger
Jim Krueger is an American comic book writer best known for his work on titles like "Earth X" and other Marvel and DC projects, often in collaboration with artist Alex Ross.
-
E.
Barry Kroeger
Barry Kroeger was an American character actor known for his distinctive villainous roles in mid-20th-century film and television.
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c321f848190a4443aa6bb4e57d7 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 3:29 p.m.