Triple
T21448500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outlaws and Angels |
E529141
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Rosanne Korenberg
Rosanne Korenberg is a film producer known for her work on independent and genre films, including the Western thriller "Outlaws and Angels."
|
E1485527
|
NE FINISHED |
How this triple was built (4 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: Rosanne Korenberg | Statement: [Outlaws and Angels, producer, Rosanne Korenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosanne Korenberg Context triple: [Outlaws and Angels, producer, Rosanne Korenberg]
-
A.
Jodi Wexler
Jodi Wexler is an actress best known for her role in the film "The Love Machine."
-
B.
Jeannine Renshaw
Jeannine Renshaw is a television producer and writer known for her work on series such as "Good Girls."
-
C.
Pamela Redmond Satran
Pamela Redmond Satran is an American author and journalist best known for her novels and for co-creating the book that inspired the television series "Younger."
-
D.
Ellen Mirojnick
Ellen Mirojnick is an American costume designer known for her influential work on films such as "Basic Instinct" and numerous other high-profile productions.
-
E.
Donna Dubrow
Donna Dubrow is a film producer best known for her work in the 1990s and for her former marriage to action director John McTiernan.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rosanne Korenberg Triple: [Outlaws and Angels, producer, Rosanne Korenberg]
Generated description
Rosanne Korenberg is a film producer known for her work on independent and genre films, including the Western thriller "Outlaws and Angels."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rosanne Korenberg Target entity description: Rosanne Korenberg is a film producer known for her work on independent and genre films, including the Western thriller "Outlaws and Angels."
-
A.
Jodi Wexler
Jodi Wexler is an actress best known for her role in the film "The Love Machine."
-
B.
Jeannine Renshaw
Jeannine Renshaw is a television producer and writer known for her work on series such as "Good Girls."
-
C.
Pamela Redmond Satran
Pamela Redmond Satran is an American author and journalist best known for her novels and for co-creating the book that inspired the television series "Younger."
-
D.
Ellen Mirojnick
Ellen Mirojnick is an American costume designer known for her influential work on films such as "Basic Instinct" and numerous other high-profile productions.
-
E.
Donna Dubrow
Donna Dubrow is a film producer best known for her work in the 1990s and for her former marriage to action director John McTiernan.
- F. None of above. chosen
Provenance (5 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d11ca48190aafe25c97dfa5578 |
completed | April 23, 2026, 9:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c907cb9c8190b4a50551b274a915 |
completed | May 17, 2026, 1:56 p.m. |
| NEDg | Description generation | batch_6a09ca21f52481909aa6033dbe4c5985 |
completed | May 17, 2026, 2:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09cb9587a88190b0e1c0d25d3733da |
completed | May 17, 2026, 2:07 p.m. |
Created at: April 16, 2026, 6:06 p.m.