Triple
T21166122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Justice League: War |
E521567
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Kevin Kliesch |
—
|
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: Kevin Kliesch | Statement: [Justice League: War, composer, Kevin Kliesch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Kliesch Context triple: [Justice League: War, composer, Kevin Kliesch]
-
A.
Kevin Kliesch
chosen
Kevin Kliesch is an American composer and orchestrator known for his work on animated films and television series, including Disney projects.
-
B.
Matthias Koenigswieser
Matthias Koenigswieser is a cinematographer known for his work on feature films such as the live-action Disney movie "Christopher Robin."
-
C.
Alexander Kudlich
Alexander Kudlich is a German technology executive and investor best known for his senior leadership role at startup incubator Rocket Internet, where he helped scale numerous global online businesses.
-
D.
Kai Wiesinger
Kai Wiesinger is a German actor known for his roles in film and television, often appearing in historical dramas and popular German cinema.
-
E.
Kevin Riepl
Kevin Riepl is an American composer best known for his atmospheric scores for films and video games, including work on titles like Gears of War and various horror and sci-fi projects.
- 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_69e0b50e30748190b186824a206d39b9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7270efe3081908a50fc601c2f958c |
completed | April 21, 2026, 7:28 a.m. |
Created at: April 16, 2026, 2:59 p.m.