Triple
T20389709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Age of Adaline |
E498053
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Tom Rosenberg |
—
|
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 Rosenberg | Statement: [The Age of Adaline, producer, Tom Rosenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Rosenberg Context triple: [The Age of Adaline, 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.
William Rosenberg
William Rosenberg was an American entrepreneur best known for creating the coffee and doughnut chain that became Dunkin' Donuts.
-
C.
Ken Rosenberg
Ken Rosenberg is a neurotic, fast-talking criminal lawyer and associate of the protagonist in the Grand Theft Auto series, most prominently featured in Vice City.
-
D.
Dave Rosenberg
Dave Rosenberg is a technology entrepreneur best known as a co-founder of MuleSoft, a leading integration and API management platform company.
-
E.
Don Ressler
Don Ressler is an American entrepreneur and co-founder of several online fashion and e-commerce ventures, best known for building subscription-based retail brands.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790e65a081909832855758fffd14 |
completed | April 20, 2026, 7:05 p.m. |
Created at: April 16, 2026, 11:28 a.m.