Triple
T9801117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mickey Rourke |
E237839
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mickey Rourke |
E237839
|
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: Mickey Rourke | Statement: [Mickey Rourke, name, Mickey Rourke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mickey Rourke Context triple: [Mickey Rourke, name, Mickey Rourke]
-
A.
Mickey Rourke
chosen
Mickey Rourke is an American actor and former boxer known for his intense, brooding performances in films such as "The Wrestler," "9½ Weeks," and "Angel Heart."
-
B.
Tim Roth
Tim Roth is an English actor known for his intense, often villainous roles in films such as "Reservoir Dogs," "Pulp Fiction," and "Rob Roy," as well as his collaborations with directors like Quentin Tarantino.
-
C.
Val Kilmer
Val Kilmer is an American actor known for his versatile performances in films such as "Top Gun," "The Doors," and "Batman Forever."
-
D.
Michael Clarke Duncan
Michael Clarke Duncan was an American actor best known for his Oscar-nominated performance as the gentle giant John Coffey in the film "The Green Mile."
-
E.
Nicolas Cage
Nicolas Cage is an American actor known for his intense and eclectic performances across action, drama, and independent 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_69ca84dd4608819097ff4ed00feca280 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda62a11a88190880e0cce24923b14 |
completed | April 1, 2026, 11:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c44a652c81908f644e1a5efe3eb1 |
completed | April 5, 2026, 2:09 a.m. |
Created at: March 30, 2026, 8:29 p.m.