Triple
T2517392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catch Me If You Can |
E55443
|
entity |
| Predicate | starred |
P5563
|
FINISHED |
| Object | Tom Hanks |
E10383
|
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: Tom Hanks | Statement: [Catch Me If You Can, starred, Tom Hanks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Hanks Context triple: [Catch Me If You Can, starred, Tom Hanks]
-
A.
Tom Hanks
chosen
Tom Hanks is an acclaimed American actor and filmmaker renowned for his versatile performances in films such as "Forrest Gump," "Saving Private Ryan," and "Cast Away."
-
B.
George Clooney
George Clooney is an American actor, filmmaker, and activist renowned for his work in film and television as well as his humanitarian and political advocacy.
-
C.
Richard Gere
Richard Gere is an American actor known for his leading roles in films such as "American Gigolo," "An Officer and a Gentleman," and "Pretty Woman."
-
D.
Gil Bellows
Gil Bellows is a Canadian actor best known for his roles in films like The Shawshank Redemption and the television series Ally McBeal.
-
E.
Kevin Bacon
Kevin Bacon is an American actor and producer known for his versatile film and television roles and for inspiring the pop-culture concept of "Six Degrees of Kevin Bacon."
- 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_69af2b9ef8048190bafff3f1853321cb |
completed | March 9, 2026, 8:20 p.m. |
Created at: March 6, 2026, 9:46 p.m.