Triple
T18634598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Gigolo |
E455510
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Julian Kaye
Julian Kaye is a charismatic Los Angeles male escort whose luxurious lifestyle unravels when he becomes entangled in a murder investigation.
|
E1336137
|
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: Julian Kaye | Statement: [American Gigolo, mainCharacter, Julian Kaye]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julian Kaye Context triple: [American Gigolo, mainCharacter, Julian Kaye]
-
A.
Elliott Landy
Elliott Landy is an American photographer best known for his iconic images of 1960s rock musicians and the Woodstock festival.
-
B.
David Kaye
David Kaye is a Canadian voice actor best known for his extensive work in animation and video games, including major roles in the Transformers franchise.
-
C.
James Kari
James Kari is a linguist known for his extensive documentation and analysis of Athabaskan languages, particularly the Koyukon language of Alaska.
-
D.
Mark Landon
Mark Landon was the guitarist for the 1960s American garage rock band The Music Machine.
-
E.
Alan Cline
Alan Cline is a computer scientist and academic known for his long-standing faculty role in the Department of Computer Science at the University of Texas at Austin.
- 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: Julian Kaye Triple: [American Gigolo, mainCharacter, Julian Kaye]
Generated description
Julian Kaye is a charismatic Los Angeles male escort whose luxurious lifestyle unravels when he becomes entangled in a murder investigation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Julian Kaye Target entity description: Julian Kaye is a charismatic Los Angeles male escort whose luxurious lifestyle unravels when he becomes entangled in a murder investigation.
-
A.
Elliott Landy
Elliott Landy is an American photographer best known for his iconic images of 1960s rock musicians and the Woodstock festival.
-
B.
David Kaye
David Kaye is a Canadian voice actor best known for his extensive work in animation and video games, including major roles in the Transformers franchise.
-
C.
James Kari
James Kari is a linguist known for his extensive documentation and analysis of Athabaskan languages, particularly the Koyukon language of Alaska.
-
D.
Mark Landon
Mark Landon was the guitarist for the 1960s American garage rock band The Music Machine.
-
E.
Alan Cline
Alan Cline is a computer scientist and academic known for his long-standing faculty role in the Department of Computer Science at the University of Texas at Austin.
- 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fc80b308190932303231524d372 |
completed | April 19, 2026, 9:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0517156f208190bf1071e86e3eaa46 |
completed | May 14, 2026, 12:28 a.m. |
| NEDg | Description generation | batch_6a05185f550081909a284d833f992da4 |
completed | May 14, 2026, 12:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05190b081481909af7b7aec70d9646 |
completed | May 14, 2026, 12:36 a.m. |
Created at: April 10, 2026, 11:46 a.m.