Triple
T22573656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roger Greenberg |
E544336
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Ben Stiller |
—
|
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: Ben Stiller | Statement: [Roger Greenberg, portrayedBy, Ben Stiller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ben Stiller Context triple: [Roger Greenberg, portrayedBy, Ben Stiller]
-
A.
Ben Stiller
chosen
Ben Stiller is an American actor, comedian, and filmmaker known for his leading roles in popular comedy films such as "Zoolander," "Meet the Parents," and "There's Something About Mary."
-
B.
Owen Wilson
Owen Wilson is an American actor and screenwriter known for his laid-back charm and roles in popular comedies and adventure films such as "Wedding Crashers," "Zoolander," and "Midnight in Paris."
-
C.
Owen Wilson
Owen Wilson is an Australian business executive best known as the CEO of REA Group, a leading digital real estate company.
-
D.
Luke Wilson
Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
-
E.
Vince Vaughn
Vince Vaughn is an American actor and comedian known for his roles in hit comedies such as "Wedding Crashers," "Dodgeball," and "Old School."
- 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_69e11e30d05481909df915354c89f0d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f15fea683c81908fbf9f171eed3341 |
completed | April 29, 2026, 1:33 a.m. |
Created at: April 16, 2026, 8:53 p.m.