Triple
T22975713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Bean’s Holiday |
E571309
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Karel Roden |
—
|
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: Karel Roden | Statement: [Mr. Bean’s Holiday, starring, Karel Roden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karel Roden Context triple: [Mr. Bean’s Holiday, starring, Karel Roden]
-
A.
Karel Roden
chosen
Karel Roden is a Czech actor known internationally for his roles in films such as "Hellboy," "The Bourne Supremacy," and various European and Hollywood productions.
-
B.
Oskar Nedbal
Oskar Nedbal was a Czech violist, conductor, and composer of the late Romantic era, known especially for his operettas and orchestral works.
-
C.
Karol Gregor
Karol Gregor is a machine learning researcher known for his work on deep reinforcement learning and representation learning, including the development of Universal Value Function Approximators.
-
D.
John Capek
John Capek is a songwriter and composer best known for co-writing the hit song "Rhythm of My Heart," popularized by Rod Stewart.
-
E.
Oskar Karlweis
Oskar Karlweis was an Austrian-born stage and film actor known for his character roles in European cinema and later in Hollywood productions.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18235de508190ab9675d005870ff6 |
completed | April 29, 2026, 3:59 a.m. |
Created at: April 17, 2026, 3:48 p.m.