Triple
T8766892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wonderland (2003 film) |
E208359
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Val Kilmer |
E97275
|
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: Val Kilmer | Statement: [Wonderland (2003 film), starring, Val Kilmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Val Kilmer Context triple: [Wonderland (2003 film), starring, Val Kilmer]
-
A.
Val Kilmer
chosen
Val Kilmer is an American actor known for his versatile performances in films such as "Top Gun," "The Doors," and "Batman Forever."
-
B.
Nicolas Cage
Nicolas Cage is an American actor known for his intense and eclectic performances across action, drama, and independent films.
-
C.
Jason Patric
Jason Patric is an American film and stage actor known for roles in movies such as "The Lost Boys," "Sleepers," and "Speed 2: Cruise Control."
-
D.
James Remar
James Remar is an American character actor known for his intense and often villainous roles in film and television, including notable performances in projects like "Dexter," "The Warriors," and "48 Hrs."
-
E.
Mickey Rourke
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."
- 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_69ca835df7e08190ac875664cca8f9ca |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5eeae70c8190adc2bc2846da0fbf |
completed | March 31, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf51a78a98819083ed4e214cd1fd22 |
completed | April 3, 2026, 5:35 a.m. |
Created at: March 30, 2026, 6:41 p.m.