Triple
T3237121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taxi to the Dark Side |
E67880
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Sari Gilman
Sari Gilman is a film editor best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
|
E444921
|
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: Sari Gilman | Statement: [Taxi to the Dark Side, editor, Sari Gilman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sari Gilman Context triple: [Taxi to the Dark Side, editor, Sari Gilman]
-
A.
Janet Margolin
Janet Margolin was an American film and television actress best known for her roles in movies such as "David and Lisa" and Woody Allen's "Annie Hall."
-
B.
Judith Gellman
Judith Gellman is a costume designer best known for her work on the 1995 film adaptation of "A Little Princess."
-
C.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
-
D.
Marla Lerner Tanenbaum
Marla Lerner Tanenbaum is an American philanthropist and baseball executive best known as a principal owner of the Washington Nationals and for her leadership in charitable and community initiatives.
-
E.
June Preisser
June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
- 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: Sari Gilman Triple: [Taxi to the Dark Side, editor, Sari Gilman]
Generated description
Sari Gilman is a film editor best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sari Gilman Target entity description: Sari Gilman is a film editor best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
-
A.
Janet Margolin
Janet Margolin was an American film and television actress best known for her roles in movies such as "David and Lisa" and Woody Allen's "Annie Hall."
-
B.
Judith Gellman
Judith Gellman is a costume designer best known for her work on the 1995 film adaptation of "A Little Princess."
-
C.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
-
D.
Marla Lerner Tanenbaum
Marla Lerner Tanenbaum is an American philanthropist and baseball executive best known as a principal owner of the Washington Nationals and for her leadership in charitable and community initiatives.
-
E.
June Preisser
June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef29bf48190a9aa3a39f0138428 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b66b1c9cb881908df6998f752f13d0 |
completed | March 15, 2026, 8:17 a.m. |
| NEDg | Description generation | batch_69b66cc2f0a081909c3021683ba6c791 |
completed | March 15, 2026, 8:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b66d36218481908dd59c49d3d55b71 |
completed | March 15, 2026, 8:26 a.m. |
Created at: March 8, 2026, 3:08 p.m.