Triple
T13257910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malik Yoba |
E315709
|
entity |
| Predicate | notableRole |
P22
|
FINISHED |
| Object | Yul Brenner |
E668512
|
NE FINISHED |
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: Yul Brenner | Statement: [Malik Yoba, notableRole, Yul Brenner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yul Brenner Context triple: [Malik Yoba, notableRole, Yul Brenner]
-
A.
Yul Brenner
chosen
Yul Brenner is a tough, determined Jamaican bobsledder in the film "Cool Runnings," known for his intimidating demeanor and underlying vulnerability.
-
B.
Troy Steiner
Troy Steiner is a former American collegiate wrestler best known as an NCAA champion and All-American for the University of Iowa under legendary coach Dan Gable.
-
C.
Anton Lesser
Anton Lesser is a British actor known for his work in film, television, and theatre, including notable roles in series such as "Game of Thrones," "Endeavour," and "The Crown."
-
D.
Gregory Bernstein
Gregory Bernstein is a film and television screenwriter known for his work on projects such as the political thriller "Official Secrets."
-
E.
George Nader
George Nader was an American film and television actor best known for his roles in 1950s Hollywood productions and later European genre films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f7614fc8190a1cac076d706e9aa |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a444b4c8190a5dd95460ac96cc7 |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:25 p.m.