Triple
T2517399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catch Me If You Can |
E55443
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Michael Kahn |
E255464
|
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: Michael Kahn | Statement: [Catch Me If You Can, editor, Michael Kahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Kahn Context triple: [Catch Me If You Can, editor, Michael Kahn]
-
A.
Michael Kahn
chosen
Michael Kahn is an acclaimed American film editor best known for his long-time collaboration with director Steven Spielberg on numerous major films.
-
B.
Marty Katz
Marty Katz is a film producer known for his work on movies such as the World War II drama "The Great Raid."
-
C.
Andrew Weisblum
Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
-
D.
Greg Kaplan
Greg Kaplan is an economist known for his research on household heterogeneity, consumption, and macroeconomic policy, and for his contributions to modern macroeconomic modeling.
-
E.
Hal Bidlack
Hal Bidlack is an American political science professor, retired U.S. Air Force officer, and public speaker known for his work in skepticism and secular humanism.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2111d28819099884a2bec5e0366 |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3a133848190a8585a4d9cde0396 |
completed | March 12, 2026, 5:10 p.m. |
Created at: March 6, 2026, 9:46 p.m.