Triple
T18289743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Man Who Came Back (1924 film) |
E438079
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Tom Santschi |
—
|
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: Tom Santschi | Statement: [The Man Who Came Back (1924 film), stars, Tom Santschi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Santschi Context triple: [The Man Who Came Back (1924 film), stars, Tom Santschi]
-
A.
Tom Santschi
chosen
Tom Santschi was an American silent film actor best known for his rugged roles in early Westerns and adventure serials.
-
B.
Tom Schaul
Tom Schaul is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Dueling DQN architecture.
-
C.
John Zulberti
John Zulberti is a former standout lacrosse player best known for his collegiate career with the Syracuse University men's lacrosse program.
-
D.
Michael Tuchner
Michael Tuchner was a British film and television director known for his work on crime dramas and character-driven stories in the 1960s and 1970s.
-
E.
Frank Teschemacher
Frank Teschemacher was an influential early Chicago jazz clarinetist and saxophonist known for his role in shaping the Chicago style of the 1920s and early 1930s.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500fd65888190afdbb29dc60066af |
completed | April 19, 2026, 4:21 p.m. |
Created at: April 10, 2026, 10:35 a.m.