Triple
T13121527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Karate Kid |
E311732
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Robert Mark Kamen |
E673171
|
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: Robert Mark Kamen | Statement: [The Karate Kid, screenwriter, Robert Mark Kamen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Mark Kamen Context triple: [The Karate Kid, screenwriter, Robert Mark Kamen]
-
A.
Robert Mark Kamen
chosen
Robert Mark Kamen is an American screenwriter and producer best known for creating and writing hit action franchises such as The Karate Kid and Taken.
-
B.
Jason Kingsley
Jason Kingsley is a British entrepreneur and game developer best known as the co-founder and CEO of the video game studio Rebellion Developments.
-
C.
Stephen Harrison
Stephen Harrison is a film editor known for his work on classic British cinema, including the historical drama "The Private Life of Henry VIII."
-
D.
Christopher Henderson
Christopher Henderson is a fictional high-ranking counterterrorism operative and former mentor to Jack Bauer in the television series "24."
-
E.
Richard Marden
Richard Marden was a British film editor known for his work on notable mid-20th-century films, including adaptations of classic literature.
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9819840b881909b76022b4c4dcaed |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e286de608190bf46af2eb656bb79 |
completed | May 3, 2026, 5:52 a.m. |
Created at: April 9, 2026, 9:06 p.m.