Triple
T7560736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Million Dollar Baby |
E178787
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Tom Rosenberg |
E359573
|
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 Rosenberg | Statement: [Million Dollar Baby, producer, Tom Rosenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Rosenberg Context triple: [Million Dollar Baby, producer, Tom Rosenberg]
-
A.
Tom Rosenberg
chosen
Tom Rosenberg is an American film producer and co-founder of Lakeshore Entertainment, known for backing numerous successful Hollywood films.
-
B.
Dave Rosenberg
Dave Rosenberg is a technology entrepreneur best known as a co-founder of MuleSoft, a leading integration and API management platform company.
-
C.
Michael Rosenberg
Michael Rosenberg is a television producer and executive known for his work on the Western drama series "Hell on Wheels."
-
D.
Mark Rosenberg
Mark Rosenberg was an American film producer known for his work on notable movies of the 1980s and early 1990s.
-
E.
Michael Vavitch
Michael Vavitch was a silent-era film actor known for his role in the 1924 drama "The Red Lily."
- 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_69c69f2f80288190b95cceb4da92ab2b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8f847c48190a1081aa9de7ff945 |
completed | March 27, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8e575c22481908a6779f5d496bd3a |
completed | March 29, 2026, 8:40 a.m. |
Created at: March 27, 2026, 3:50 p.m.