Triple
T16264846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rush Hour |
E394848
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Mark Rolston |
E996941
|
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: Mark Rolston | Statement: [Rush Hour, starring, Mark Rolston]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Rolston Context triple: [Rush Hour, starring, Mark Rolston]
-
A.
Mark Rolston
chosen
Mark Rolston is an American character actor known for his intense roles in films such as Aliens, The Shawshank Redemption, and numerous genre and action movies.
-
B.
Matthew Rolston
Matthew Rolston is an American photographer and director renowned for his stylized celebrity portraiture and visually distinctive music videos.
-
C.
Mark Okerstrom
Mark Okerstrom is a Canadian business executive best known for serving as the former CEO of Expedia Group.
-
D.
Matthew Rolph
Matthew Rolph is an American actor and comedian best known for his marriage to actress and comedian Mary Lynn Rajskub.
-
E.
Mark Romberg
Mark Romberg is an individual associated with the use or application of a system, tool, or concept referred to as Romberg.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c73944819085633e6d2a69bae9 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a002da0a2908190923e61bdeb92567d |
completed | May 10, 2026, 7:02 a.m. |
Created at: April 10, 2026, 5:05 a.m.