Triple
T4666998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rush (2013 film) |
E102868
|
entity |
| Predicate | portraysPerson |
P1852
|
FINISHED |
| Object |
Suzy Miller
Suzy Miller is a former British model and socialite best known for her high-profile marriages to Formula One driver James Hunt and later actor Richard Burton.
|
E482373
|
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: Suzy Miller | Statement: [Rush (2013 film), portraysPerson, Suzy Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzy Miller Context triple: [Rush (2013 film), portraysPerson, Suzy Miller]
-
A.
Lorraine Miller
Lorraine Miller was an American actress and dancer active in Hollywood films during the 1940s and 1950s.
-
B.
Tamara Miller
Tamara Miller is a member of the Disney family and a granddaughter of Walt Disney through his daughter Diane Disney Miller.
-
C.
Suzanne Johnson
Suzanne Johnson is a member of the Johnson family, known primarily in relation to that family group.
-
D.
Betsy McCaughey
Betsy McCaughey is an American politician, writer, and former Lieutenant Governor of New York known for her conservative commentary and opposition to certain health care reforms.
-
E.
Mary Beth Hughes
Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
- 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: Suzy Miller Triple: [Rush (2013 film), portraysPerson, Suzy Miller]
Generated description
Suzy Miller is a former British model and socialite best known for her high-profile marriages to Formula One driver James Hunt and later actor Richard Burton.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suzy Miller Target entity description: Suzy Miller is a former British model and socialite best known for her high-profile marriages to Formula One driver James Hunt and later actor Richard Burton.
-
A.
Lorraine Miller
Lorraine Miller was an American actress and dancer active in Hollywood films during the 1940s and 1950s.
-
B.
Tamara Miller
Tamara Miller is a member of the Disney family and a granddaughter of Walt Disney through his daughter Diane Disney Miller.
-
C.
Suzanne Johnson
Suzanne Johnson is a member of the Johnson family, known primarily in relation to that family group.
-
D.
Betsy McCaughey
Betsy McCaughey is an American politician, writer, and former Lieutenant Governor of New York known for her conservative commentary and opposition to certain health care reforms.
-
E.
Mary Beth Hughes
Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
- 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_69bd43d9cba4819086c1ab1c2d9d2133 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6c3d1cb88190a42919dcbfe2568c |
completed | March 20, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81a32f18819093c08d05039442c4 |
completed | March 21, 2026, 11:31 a.m. |
| NEDg | Description generation | batch_69be84733e0081908c4787d4be73d8c5 |
completed | March 21, 2026, 11:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be84c3905c8190b87f685607092a20 |
completed | March 21, 2026, 11:45 a.m. |
Created at: March 20, 2026, 1:15 p.m.