Triple
T21699479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mediaset |
E535616
|
entity |
| Predicate | owns |
P347
|
FINISHED |
| Object | Medusa 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: Medusa Film | Statement: [Mediaset, owns, Medusa Film]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Medusa Film Context triple: [Mediaset, owns, Medusa Film]
-
A.
Medusa Film
chosen
Medusa Film is an Italian film production and distribution company known for backing a wide range of domestic and international movies.
-
B.
Galatea Film
Galatea Film is an Italian film production company known for backing genre and horror movies, including works by director Mario Bava.
-
C.
Mantaray Film
Mantaray Film is a Swedish film production company known for producing acclaimed documentaries and feature films, often with a strong focus on personal and artistic stories.
-
D.
Athos Films
Athos Films is a French film distribution company known for handling the release of notable art-house and New Wave films.
-
E.
Diaphana Films
Diaphana Films is a French film distribution and production company known for handling acclaimed international and auteur cinema.
- 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_69e0c46a6ee481908836e1420fb78c9b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef9b7de9888190bbca7717a32f9888 |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 16, 2026, 6:45 p.m.