Triple
T9735495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waiting for Superman |
E236046
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Kim Roberts
Kim Roberts is a film editor known for her work on the documentary "Waiting for Superman."
|
E828701
|
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: Kim Roberts | Statement: [Waiting for Superman, editor, Kim Roberts]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Roberts Context triple: [Waiting for Superman, editor, Kim Roberts]
-
A.
Jill Eikenberry
Jill Eikenberry is an American actress best known for her Emmy-nominated role as attorney Ann Kelsey on the television series "L.A. Law."
-
B.
Nancy Allen
Nancy Allen is an American actress best known for her roles in films such as "Carrie," "Dressed to Kill," and the "RoboCop" series.
-
C.
Danielle Kaye
Danielle Kaye is known as the spouse of British film director and music video creator Tony Kaye.
-
D.
Grace Van Patten
Grace Van Patten is an American actress known for her nuanced performances in independent films and television series, including notable roles in projects like "The Meyerowitz Stories" and "Nine Perfect Strangers."
-
E.
Joan Allen
Joan Allen is an acclaimed American actress known for her versatile performances in film, television, and theater, including prominent roles in dramas and political thrillers.
- 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: Kim Roberts Triple: [Waiting for Superman, editor, Kim Roberts]
Generated description
Kim Roberts is a film editor known for her work on the documentary "Waiting for Superman."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kim Roberts Target entity description: Kim Roberts is a film editor known for her work on the documentary "Waiting for Superman."
-
A.
Jill Eikenberry
Jill Eikenberry is an American actress best known for her Emmy-nominated role as attorney Ann Kelsey on the television series "L.A. Law."
-
B.
Nancy Allen
Nancy Allen is an American actress best known for her roles in films such as "Carrie," "Dressed to Kill," and the "RoboCop" series.
-
C.
Danielle Kaye
Danielle Kaye is known as the spouse of British film director and music video creator Tony Kaye.
-
D.
Grace Van Patten
Grace Van Patten is an American actress known for her nuanced performances in independent films and television series, including notable roles in projects like "The Meyerowitz Stories" and "Nine Perfect Strangers."
-
E.
Joan Allen
Joan Allen is an acclaimed American actress known for her versatile performances in film, television, and theater, including prominent roles in dramas and political thrillers.
- 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eee70d48190af5a833d7b33aaa5 |
completed | April 1, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d048c5081908c891633129dc5d6 |
completed | April 5, 2026, 7:19 a.m. |
| NEDg | Description generation | batch_69d20e9f480c819086b0165aa77ddb06 |
completed | April 5, 2026, 7:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d20fa9cab88190bbddcf18b49f8172 |
completed | April 5, 2026, 7:30 a.m. |
Created at: March 30, 2026, 8:22 p.m.