Triple
T12683525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hesher |
E303006
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Jennifer Roth
Jennifer Roth is a film producer known for her work on independent and character-driven movies.
|
E1034767
|
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: Jennifer Roth | Statement: [Hesher, producer, Jennifer Roth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Roth Context triple: [Hesher, producer, Jennifer Roth]
-
A.
Melissa Rosenberg
Melissa Rosenberg is an American screenwriter and producer best known for adapting the Twilight Saga films and creating the Marvel television series Jessica Jones.
-
B.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
C.
Jena Friedman
Jena Friedman is an American comedian, writer, and filmmaker known for her dark, satirical political humor and work on projects like The Daily Show and her special "American Cunt."
-
D.
Rachel Taub
Rachel Taub is a fictional character from the medical drama series "House," known as the wife of Dr. Chris Taub.
-
E.
Lisa Weinstein
Lisa Weinstein is a film producer best known for her work on the acclaimed 1990 romantic fantasy drama "Ghost."
- 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: Jennifer Roth Triple: [Hesher, producer, Jennifer Roth]
Generated description
Jennifer Roth is a film producer known for her work on independent and character-driven movies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jennifer Roth Target entity description: Jennifer Roth is a film producer known for her work on independent and character-driven movies.
-
A.
Melissa Rosenberg
Melissa Rosenberg is an American screenwriter and producer best known for adapting the Twilight Saga films and creating the Marvel television series Jessica Jones.
-
B.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
C.
Jena Friedman
Jena Friedman is an American comedian, writer, and filmmaker known for her dark, satirical political humor and work on projects like The Daily Show and her special "American Cunt."
-
D.
Rachel Taub
Rachel Taub is a fictional character from the medical drama series "House," known as the wife of Dr. Chris Taub.
-
E.
Lisa Weinstein
Lisa Weinstein is a film producer best known for her work on the acclaimed 1990 romantic fantasy drama "Ghost."
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d68358819095bdaab8adf1dcf0 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f0a5a58819082111550a65a04b9 |
completed | May 3, 2026, 10:10 a.m. |
| NEDg | Description generation | batch_69f720861728819088bc43753d19bef8 |
completed | May 3, 2026, 10:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7212ca3f48190915d73f987ec60d2 |
completed | May 3, 2026, 10:19 a.m. |
Created at: April 9, 2026, 5:21 p.m.