Triple
T21736208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medicine Man |
E536530
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Tom Schulman |
—
|
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 Schulman | Statement: [Medicine Man, screenwriter, Tom Schulman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Schulman Context triple: [Medicine Man, screenwriter, Tom Schulman]
-
A.
Tom Schulman
chosen
Tom Schulman is an American screenwriter best known for writing the Academy Award–winning screenplay for the film "Dead Poets Society."
-
B.
Adam Shulman
Adam Shulman is an American actor and jewelry designer best known as the husband of actress Anne Hathaway.
-
C.
Ben Feldman
Ben Feldman is an American actor known for his roles in television series such as Drop Dead Diva, Mad Men, and Superstore.
-
D.
Todd Schulman
Todd Schulman is a film producer known for working on comedy and action projects, including collaborations with Sacha Baron Cohen.
-
E.
David Margulies
David Margulies was an American character actor known for his roles in films such as Ghostbusters and numerous appearances on stage and television.
- 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69effd0c0a088190bd1926fa4b73d8f4 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 16, 2026, 6:49 p.m.