Triple
T21445748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Karlsen |
E529070
|
entity |
| Predicate | coFounderOf |
P104
|
FINISHED |
| Object | Number 9 Films Ltd |
—
|
NE NERFINISHED |
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: Number 9 Films Ltd | Statement: [Elizabeth Karlsen, coFounderOf, Number 9 Films Ltd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Number 9 Films Ltd Context triple: [Elizabeth Karlsen, coFounderOf, Number 9 Films Ltd]
-
A.
Number 9 Films
chosen
Number 9 Films is a British film production company known for producing acclaimed independent and arthouse films.
-
B.
Nala Films
Nala Films is an independent film production company known for financing and producing critically acclaimed feature films.
-
C.
Rook Films
Rook Films is a British independent film production company known for its distinctive, often surreal and genre-bending movies.
-
D.
Imagine Films
Imagine Films is a film production division associated with the American entertainment company Imagine Entertainment, known for developing and producing motion pictures.
-
E.
Apache Films
Apache Films is a Spanish film production company known for backing genre and auteur-driven movies such as the psychological thriller "Marrowbone."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b707ecd88190b3576b8923840870 |
completed | April 22, 2026, 11:54 a.m. |
Created at: April 16, 2026, 6:05 p.m.