Triple
T15940331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rebecca O'Brien |
E386540
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Sixteen Films |
E257845
|
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: Sixteen Films | Statement: [Rebecca O'Brien, employer, Sixteen Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sixteen Films Context triple: [Rebecca O'Brien, employer, Sixteen Films]
-
A.
Sixteen Films
chosen
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.
Cinema 16
Cinema 16 was a pioneering New York film society and showcase for independent, experimental, and documentary cinema in the mid-20th century.
-
C.
Les Films 13
Les Films 13 is a French film production company known for backing notable auteur-driven cinema.
-
D.
Capital Film
Capital Film is an Italian film production company known for producing genre movies during the 1960s and 1970s.
-
E.
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.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156cd3a188190a1a7dcbfdd38284c |
completed | April 16, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe7455c48190bfad24eb8905426d |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:53 a.m.