Triple
T20517478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Equals |
E503717
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Michael Pruss |
—
|
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: Michael Pruss | Statement: [Equals, producer, Michael Pruss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Pruss Context triple: [Equals, producer, Michael Pruss]
-
A.
Michael A. Pruss
Michael A. Pruss is a film producer known for his work on critically acclaimed dramas, including collaborations with major directors on projects such as "Empire of Light."
-
B.
Michaël Pruss
chosen
Michaël Pruss is a film and television producer known for his work on high-profile international projects, including the espionage drama series "Berlin Station."
-
C.
Michael Grunst
Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
-
D.
Erich Klein
Erich Klein was the son of influential psychoanalyst Melanie Klein, about whom relatively little is documented beyond his familial connection.
-
E.
Michael Engelhart
Michael Engelhart was an architect known for his role in the post-war reconstruction of Vienna’s historic Burgtheater.
- 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_69e0b4b2aa788190ae9eb37c1d73b1f1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f42db688190a3ccfba5601e8bf3 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 16, 2026, 11:36 a.m.