Triple
T8415466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thelma & Louise |
E198719
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Mimi Polk Gitlin
Mimi Polk Gitlin is a film producer best known for her work on the acclaimed 1991 road movie "Thelma & Louise."
|
E732989
|
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: Mimi Polk Gitlin | Statement: [Thelma & Louise, producer, Mimi Polk Gitlin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mimi Polk Gitlin Context triple: [Thelma & Louise, producer, Mimi Polk Gitlin]
-
A.
Ann Turkel
Ann Turkel is an American actress and former model known for her film and television roles in the 1970s and 1980s.
-
B.
Toni Stern
Toni Stern is an American lyricist best known for co-writing several of Carole King’s classic songs, including major tracks on the landmark album "Tapestry."
-
C.
Elissa Durwood Grodin
Elissa Durwood Grodin is an American author known for writing mystery novels and children's books.
-
D.
Carolee Joyce Winstein
Carolee Joyce Winstein is an American neuroscientist and rehabilitation researcher known for her work on motor control and recovery after neurological injury.
-
E.
Debbie Cenziper
Debbie Cenziper is a Pulitzer Prize–winning investigative journalist and author known for her in-depth reporting and co-writing prominent nonfiction books.
- 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: Mimi Polk Gitlin Triple: [Thelma & Louise, producer, Mimi Polk Gitlin]
Generated description
Mimi Polk Gitlin is a film producer best known for her work on the acclaimed 1991 road movie "Thelma & Louise."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mimi Polk Gitlin Target entity description: Mimi Polk Gitlin is a film producer best known for her work on the acclaimed 1991 road movie "Thelma & Louise."
-
A.
Ann Turkel
Ann Turkel is an American actress and former model known for her film and television roles in the 1970s and 1980s.
-
B.
Toni Stern
Toni Stern is an American lyricist best known for co-writing several of Carole King’s classic songs, including major tracks on the landmark album "Tapestry."
-
C.
Elissa Durwood Grodin
Elissa Durwood Grodin is an American author known for writing mystery novels and children's books.
-
D.
Carolee Joyce Winstein
Carolee Joyce Winstein is an American neuroscientist and rehabilitation researcher known for her work on motor control and recovery after neurological injury.
-
E.
Debbie Cenziper
Debbie Cenziper is a Pulitzer Prize–winning investigative journalist and author known for her in-depth reporting and co-writing prominent nonfiction books.
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e443a08190983d9a0a61e0f781 |
completed | March 31, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0333a3488190ba30d03b1d7bacb1 |
completed | April 2, 2026, 5:48 a.m. |
| NEDg | Description generation | batch_69ce0781859c8190bb92f41c00af459b |
completed | April 2, 2026, 6:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce089d09c08190ba321aed4044a862 |
completed | April 2, 2026, 6:11 a.m. |
Created at: March 30, 2026, 6:06 p.m.