Triple
T17865556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Maze |
E446688
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Michael Pate |
—
|
NE NERFINISHED |
How this triple was built (2 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: Michael Pate | Statement: [The Maze, castMember, Michael Pate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Pate Context triple: [The Maze, castMember, Michael Pate]
-
A.
Michael Pate
chosen
Michael Pate was an Australian actor and writer known for his prolific character roles in film and television from the 1940s through the 1980s.
-
B.
Michael Pennington
Michael Pennington is a distinguished English actor and director, particularly renowned for his work in classical theatre and Shakespearean performance.
-
C.
John Bisson
John Bisson is a screenwriter best known for his work on the film "The Rage."
-
D.
Michael Pugh
Michael Pugh is a personal name shared by multiple individuals, including professionals and public figures in various fields.
-
E.
Jeffery Wood
Jeffery Wood is an American actor best known for his role on the television sitcom "In the House."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49793a2588190bb341ac606d767fe |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 10:17 a.m.