Triple
T21866170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Dolce Vita |
E539887
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Riama Film |
—
|
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: Riama Film | Statement: [La Dolce Vita, producer, Riama Film]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Riama Film Context triple: [La Dolce Vita, producer, Riama Film]
-
A.
Riama Film
chosen
Riama Film is an Italian film production company best known for producing Federico Fellini’s classic 1960 drama "La Dolce Vita."
-
B.
Xingu Films
Xingu Films is a film production company known for producing independent and art-house cinema, including the movie "Moon."
-
C.
Rhea Films
Rhea Films is a film production company known for collaborating on independent, critically acclaimed movies such as the crime thriller "Good Time."
-
D.
Aiete Films
Aiete Films is a Spanish film production company known for backing notable Spanish-language cinema such as the acclaimed historical drama "¡Ay, Carmela!".
-
E.
Nyerai Films
Nyerai Films is a Zimbabwean film production company known for creating socially conscious, women-centered stories under the leadership of writer and filmmaker Tsitsi Dangarembga.
- 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0d63f2ec48190956a3e99d8f98b1f |
completed | April 28, 2026, 3:46 p.m. |
Created at: April 16, 2026, 6:56 p.m.