Triple
T14653664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Lovers and a Bear |
E344053
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Max Films
Max Films is a Canadian film production company known for producing independent and auteur-driven movies.
|
E1112016
|
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: Max Films | Statement: [Two Lovers and a Bear, productionCompany, Max Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max Films Context triple: [Two Lovers and a Bear, productionCompany, Max Films]
-
A.
MDB Films
MDB Films is a film production company known for producing the movie "The Kingdom."
-
B.
Lux Film
Lux Film was an Italian film production company active mainly in the mid-20th century, known for producing a range of notable Italian and European films.
-
C.
Apache Films
Apache Films is a Spanish film production company known for backing genre and auteur-driven movies such as the psychological thriller "Marrowbone."
-
D.
Bay Films
Bay Films is a film production company founded by director Michael Bay, known for producing high-octane action movies and large-scale Hollywood blockbusters.
-
E.
Vivo Film
Vivo Film is an Italian independent film production company known for backing auteur-driven and socially engaged cinema, including the biographical drama "Miss Marx."
- 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: Max Films Triple: [Two Lovers and a Bear, productionCompany, Max Films]
Generated description
Max Films is a Canadian film production company known for producing independent and auteur-driven movies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Max Films Target entity description: Max Films is a Canadian film production company known for producing independent and auteur-driven movies.
-
A.
MDB Films
MDB Films is a film production company known for producing the movie "The Kingdom."
-
B.
Lux Film
Lux Film was an Italian film production company active mainly in the mid-20th century, known for producing a range of notable Italian and European films.
-
C.
Apache Films
Apache Films is a Spanish film production company known for backing genre and auteur-driven movies such as the psychological thriller "Marrowbone."
-
D.
Bay Films
Bay Films is a film production company founded by director Michael Bay, known for producing high-octane action movies and large-scale Hollywood blockbusters.
-
E.
Vivo Film
Vivo Film is an Italian independent film production company known for backing auteur-driven and socially engaged cinema, including the biographical drama "Miss Marx."
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb518f7dc8190877997ea4cd3eed2 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5db85648190b0e5b1c0827fa9f4 |
completed | May 8, 2026, 12:23 p.m. |
| NEDg | Description generation | batch_69fdd6ed699c8190919677dcdf2d24d9 |
completed | May 8, 2026, 12:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdd79198108190a3e640eca97b082b |
completed | May 8, 2026, 12:31 p.m. |
Created at: April 10, 2026, 1:27 a.m.