Triple
T10201156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crimes of the Future (2022 film) |
E238882
|
entity |
| Predicate | distributor |
P1951
|
FINISHED |
| Object |
MK2 Films
MK2 Films is a French film production and distribution company known for handling acclaimed international and auteur cinema.
|
E847398
|
NE FINISHED |
How this triple was built (4 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: MK2 Films | Statement: [Crimes of the Future (2022 film), distributor, MK2 Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MK2 Films Context triple: [Crimes of the Future (2022 film), distributor, MK2 Films]
-
A.
M6 Films
M6 Films is a French film production company known for backing popular international action and thriller movies.
-
B.
Maverick Films
Maverick Films is a film production company known for backing independent and genre-driven movies, including the crime comedy-drama "Gridlock'd."
-
C.
Mimir Films
Mimir Films is a film and television production company known for producing the sci-fi series "Silo."
-
D.
Mandeville Films
Mandeville Films is an American film and television production company known for producing a range of commercially successful and critically acclaimed movies and series.
-
E.
Pym Films
Pym Films is a film production company associated with the experimental and architectural cinema of German filmmaker Heinz Emigholz.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MK2 Films Triple: [Crimes of the Future (2022 film), distributor, MK2 Films]
Generated description
MK2 Films is a French film production and distribution company known for handling acclaimed international and auteur cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MK2 Films Target entity description: MK2 Films is a French film production and distribution company known for handling acclaimed international and auteur cinema.
-
A.
M6 Films
M6 Films is a French film production company known for backing popular international action and thriller movies.
-
B.
Maverick Films
Maverick Films is a film production company known for backing independent and genre-driven movies, including the crime comedy-drama "Gridlock'd."
-
C.
Mimir Films
Mimir Films is a film and television production company known for producing the sci-fi series "Silo."
-
D.
Mandeville Films
Mandeville Films is an American film and television production company known for producing a range of commercially successful and critically acclaimed movies and series.
-
E.
Pym Films
Pym Films is a film production company associated with the experimental and architectural cinema of German filmmaker Heinz Emigholz.
- F. None of above. chosen
Provenance (5 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_69ca84e1ea088190b38162e43d4cfa8f |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdee40cb7481908a1bf4d5636eb8ef |
completed | April 2, 2026, 4:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317f40f0c8190a3d966c934cc20f7 |
completed | April 6, 2026, 2:18 a.m. |
| NEDg | Description generation | batch_69d3188886908190ba0a5539ce942980 |
completed | April 6, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d31c4fb8288190bbc6b3d4a79dafb1 |
completed | April 6, 2026, 2:37 a.m. |
Created at: March 30, 2026, 9:14 p.m.