Triple
T17469434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eldar Ryazanov |
E425365
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Mosfilm |
—
|
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: Mosfilm | Statement: [Eldar Ryazanov, employer, Mosfilm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mosfilm Context triple: [Eldar Ryazanov, employer, Mosfilm]
-
A.
Mosfilm
chosen
Mosfilm is one of Russia’s largest and oldest film studios, renowned for producing many of the Soviet Union’s most iconic movies.
-
B.
Gorky Film Studio
Gorky Film Studio is a major Soviet and Russian film studio, historically known for producing children’s films and notable cinematic works in Moscow.
-
C.
UFA film studios
UFA film studios was a major German film production company that became a central force in shaping the innovative and influential cinema of the Weimar Republic.
-
D.
Lenfilm
Lenfilm is one of Russia’s oldest and most prominent film studios, based in Saint Petersburg and known for producing many classic Soviet-era movies.
-
E.
Nazimova Productions
Nazimova Productions was a silent-era film production company associated with the celebrated Russian-American actress and producer Alla Nazimova.
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451aad4a08190be7e25841da8e952 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.