Triple
T15971305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Pablo Tour |
E387328
|
entity |
| Predicate | stageDesigner |
P25320
|
FINISHED |
| Object |
John McGuire
John McGuire is a stage designer known for his work on major concert productions, including Kanye West’s Saint Pablo Tour.
|
E1186346
|
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: John McGuire | Statement: [Saint Pablo Tour, stageDesigner, John McGuire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John McGuire Context triple: [Saint Pablo Tour, stageDesigner, John McGuire]
-
A.
John McGuire
John McGuire was an American film actor active in the 1930s and 1940s, known for his roles in crime dramas and Westerns.
-
B.
Don McGuire
Don McGuire was an American screenwriter, director, and actor best known for co-writing the story that inspired the acclaimed comedy film "Tootsie."
-
C.
Terry McCaleb
Terry McCaleb is a retired FBI profiler and heart transplant recipient who becomes an unlikely investigator in Michael Connelly’s crime novel "Blood Work."
-
D.
Tom Howe
Tom Howe is a British composer best known for his work on film and television scores, including the acclaimed series "Ted Lasso."
-
E.
Ray Danton
Ray Danton was an American actor and director best known for his suave, often villainous roles in film and television during the 1950s and 1960s.
- 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: John McGuire Triple: [Saint Pablo Tour, stageDesigner, John McGuire]
Generated description
John McGuire is a stage designer known for his work on major concert productions, including Kanye West’s Saint Pablo Tour.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John McGuire Target entity description: John McGuire is a stage designer known for his work on major concert productions, including Kanye West’s Saint Pablo Tour.
-
A.
John McGuire
John McGuire was an American film actor active in the 1930s and 1940s, known for his roles in crime dramas and Westerns.
-
B.
Don McGuire
Don McGuire was an American screenwriter, director, and actor best known for co-writing the story that inspired the acclaimed comedy film "Tootsie."
-
C.
Terry McCaleb
Terry McCaleb is a retired FBI profiler and heart transplant recipient who becomes an unlikely investigator in Michael Connelly’s crime novel "Blood Work."
-
D.
Tom Howe
Tom Howe is a British composer best known for his work on film and television scores, including the acclaimed series "Ted Lasso."
-
E.
Ray Danton
Ray Danton was an American actor and director best known for his suave, often villainous roles in film and television during the 1950s and 1960s.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15729d73c8190a4140a0e55ee2566 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe88fa308190942d37cf67458396 |
completed | May 9, 2026, 11:08 p.m. |
| NEDg | Description generation | batch_69ffbf3f40288190a59646124e06a864 |
completed | May 9, 2026, 11:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffbfddd0348190baab794f613c71bf |
completed | May 9, 2026, 11:14 p.m. |
Created at: April 10, 2026, 4:54 a.m.