Triple
T14805707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linha de Passe |
E348028
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object | VideoFilmes |
E1081581
|
NE FINISHED |
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: VideoFilmes | Statement: [Linha de Passe, productionCompany, VideoFilmes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VideoFilmes Context triple: [Linha de Passe, productionCompany, VideoFilmes]
-
A.
VideoFilmes
chosen
VideoFilmes is a Brazilian film production company known for its work on acclaimed art-house and independent films.
-
B.
FilmFour
FilmFour is a British film production company and former television channel associated with Channel 4, known for backing distinctive independent and arthouse films.
-
C.
Flims
Flims is a Swiss alpine resort village in the canton of Graubünden, known for its skiing, hiking, and scenic mountain landscapes.
-
D.
ContentFilm
ContentFilm is a film production and distribution company known for backing independent and critically acclaimed movies such as "Thank You for Smoking."
-
E.
Home Video
Home Video is a film featuring actor and filmmaker Alex Karpovsky in its cast.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decf32666081908e84f985c47eb963 |
completed | April 14, 2026, 11:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe24c6b3008190a0fac1dace40361a |
completed | May 8, 2026, 6 p.m. |
Created at: April 10, 2026, 1:34 a.m.