Triple
T18139985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samba |
E434234
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Ten Films
Ten Films is a film production company known for producing the movie "Samba."
|
E1307920
|
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: Ten Films | Statement: [Samba, productionCompany, Ten Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ten Films Context triple: [Samba, productionCompany, Ten Films]
-
A.
Sixteen Films
Sixteen Films is a British film production company best known for producing socially and politically engaged films, frequently in collaboration with director Ken Loach.
-
B.
The Movies
The Movies is a simulation video game that lets players run a Hollywood film studio, managing production, stars, and the creation of custom movies.
-
C.
Films 59
Films 59 is a Spanish film production company best known for producing Luis Buñuel’s acclaimed 1961 drama "Viridiana."
-
D.
Flims
Flims is a Swiss alpine resort village in the canton of Graubünden, known for its skiing, hiking, and scenic mountain landscapes.
-
E.
Les Films Vog
Les Films Vog was a French film distribution company active in the mid-20th century, known for handling a variety of European feature films.
- 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: Ten Films Triple: [Samba, productionCompany, Ten Films]
Generated description
Ten Films is a film production company known for producing the movie "Samba."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ten Films Target entity description: Ten Films is a film production company known for producing the movie "Samba."
-
A.
Sixteen Films
Sixteen Films is a British film production company best known for producing socially and politically engaged films, frequently in collaboration with director Ken Loach.
-
B.
The Movies
The Movies is a simulation video game that lets players run a Hollywood film studio, managing production, stars, and the creation of custom movies.
-
C.
Films 59
Films 59 is a Spanish film production company best known for producing Luis Buñuel’s acclaimed 1961 drama "Viridiana."
-
D.
Flims
Flims is a Swiss alpine resort village in the canton of Graubünden, known for its skiing, hiking, and scenic mountain landscapes.
-
E.
Les Films Vog
Les Films Vog was a French film distribution company active in the mid-20th century, known for handling a variety of European feature films.
- 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_69d8b90aac308190801e2c57d8c5bfe5 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4de0a59d08190be74c1ecc00a8f3a |
completed | April 19, 2026, 1:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03854e76d8819082bae8d5f08e5992 |
completed | May 12, 2026, 7:53 p.m. |
| NEDg | Description generation | batch_6a038684e2908190ad31b29faabb4e46 |
completed | May 12, 2026, 7:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0386e3b51c81908c41fa719a4bf72f |
completed | May 12, 2026, 8 p.m. |
Created at: April 10, 2026, 10:29 a.m.