Triple
T12854935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sólo con tu pareja |
E307425
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Tobogán Films
Tobogán Films is a film production company known for producing the Mexican romantic comedy "Sólo con tu pareja."
|
E1006811
|
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: Tobogán Films | Statement: [Sólo con tu pareja, productionCompany, Tobogán Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tobogán Films Context triple: [Sólo con tu pareja, productionCompany, Tobogán Films]
-
A.
Tornasol Films
Tornasol Films is a Spanish film production company known for co-producing acclaimed international films, including Ken Loach’s award-winning drama "The Wind That Shakes the Barley."
-
B.
Two Ton Films
Two Ton Films is a film production company best known for producing the ensemble romantic comedy "The Big Wedding."
-
C.
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
D.
Zaftig Films
Zaftig Films is a television and film production company best known for producing the acclaimed drama series "This Is Us."
-
E.
Tobis Film
Tobis Film is a German film production and distribution company known for its role in the European cinema industry since the early 20th century.
- 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: Tobogán Films Triple: [Sólo con tu pareja, productionCompany, Tobogán Films]
Generated description
Tobogán Films is a film production company known for producing the Mexican romantic comedy "Sólo con tu pareja."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tobogán Films Target entity description: Tobogán Films is a film production company known for producing the Mexican romantic comedy "Sólo con tu pareja."
-
A.
Tornasol Films
Tornasol Films is a Spanish film production company known for co-producing acclaimed international films, including Ken Loach’s award-winning drama "The Wind That Shakes the Barley."
-
B.
Two Ton Films
Two Ton Films is a film production company best known for producing the ensemble romantic comedy "The Big Wedding."
-
C.
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
D.
Zaftig Films
Zaftig Films is a television and film production company best known for producing the acclaimed drama series "This Is Us."
-
E.
Tobis Film
Tobis Film is a German film production and distribution company known for its role in the European cinema industry since the early 20th century.
- 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_69d7bdf5e7cc8190be357278bc5ba3bb |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97021df7481909cd42a0f72040aa5 |
completed | April 10, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69ba9a53c81908e9ed120f6cb94af |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69d48e6948190a13afe3b8943d877 |
completed | May 3, 2026, 12:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69dfa2b8481908827025a28bfb056 |
completed | May 3, 2026, 12:59 a.m. |
Created at: April 9, 2026, 5:37 p.m.