Triple
T1227559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinderella Man |
E26359
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Craig Bierko |
E71819
|
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: Craig Bierko | Statement: [Cinderella Man, starring, Craig Bierko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Craig Bierko Context triple: [Cinderella Man, starring, Craig Bierko]
-
A.
Craig Bierko
chosen
Craig Bierko is an American actor known for his work in film, television, and theater, often playing charismatic or villainous roles.
-
B.
Jim Loscutoff
Jim Loscutoff was an American professional basketball forward best known for his rugged defense and seven NBA championships with the Boston Celtics in the 1950s and 1960s.
-
C.
Jesse Michaels
Jesse Michaels is an American musician and artist best known as the lead vocalist and lyricist of the influential ska punk band Operation Ivy.
-
D.
Matt Messina
Matt Messina is an American film and television composer best known for his award-winning score for the movie "Juno."
-
E.
Keith Fraase
Keith Fraase is a film editor best known for his work on the movie "Chappaquiddick."
- 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_69a49484688c8190a1bf285eb396a8b6 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be3b32a08190a36e96a3e51976eb |
completed | March 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aca2eb158081909f1175d8f4daaa96 |
completed | March 7, 2026, 10:12 p.m. |
Created at: March 1, 2026, 7:47 p.m.