Triple
T5042572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crime of Passion |
E113578
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Bill Doyle
Bill Doyle is a fictional protagonist featured in the crime drama "Crime of Passion."
|
E488979
|
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: Bill Doyle | Statement: [Crime of Passion, mainCharacter, Bill Doyle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Doyle Context triple: [Crime of Passion, mainCharacter, Bill Doyle]
-
A.
Matt O'Leary
Matt O'Leary is an American actor best known for his roles in early 2000s films such as the Spy Kids franchise and various independent and genre movies.
-
B.
Phil Burke
Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
-
C.
Jim O’Brien
Jim O’Brien is a former American football placekicker best known for kicking the game-winning field goal for the Baltimore Colts in Super Bowl V.
-
D.
John Bagley
John Bagley is a former American professional basketball player and point guard who starred at Boston College before playing in the NBA during the 1980s.
-
E.
Ed McCauley
Ed McCauley is a Canadian academic and research leader who serves as president of the University of Calgary.
- 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: Bill Doyle Triple: [Crime of Passion, mainCharacter, Bill Doyle]
Generated description
Bill Doyle is a fictional protagonist featured in the crime drama "Crime of Passion."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bill Doyle Target entity description: Bill Doyle is a fictional protagonist featured in the crime drama "Crime of Passion."
-
A.
Matt O'Leary
Matt O'Leary is an American actor best known for his roles in early 2000s films such as the Spy Kids franchise and various independent and genre movies.
-
B.
Phil Burke
Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
-
C.
Jim O’Brien
Jim O’Brien is a former American football placekicker best known for kicking the game-winning field goal for the Baltimore Colts in Super Bowl V.
-
D.
Jeff Gillooly
Jeff Gillooly is best known as the ex-husband of figure skater Tonya Harding and a central figure in the 1994 attack on her rival Nancy Kerrigan.
-
E.
John Bagley
John Bagley is a former American professional basketball player and point guard who starred at Boston College before playing in the NBA during the 1980s.
- 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73df8f7481909a8b86c4ae69aab9 |
completed | March 20, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c87455081908b759eed55730503 |
completed | March 21, 2026, 1:26 p.m. |
| NEDg | Description generation | batch_69be9fc4f8bc8190a21c828ea5cb9529 |
completed | March 21, 2026, 1:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bea026f1008190861990c7a330222e |
completed | March 21, 2026, 1:41 p.m. |
Created at: March 20, 2026, 1:37 p.m.