Triple
T20995087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Birthday Girl |
E517125
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object | FilmFour |
—
|
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: FilmFour | Statement: [Birthday Girl, productionCompany, FilmFour]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FilmFour Context triple: [Birthday Girl, productionCompany, FilmFour]
-
A.
FilmFour
chosen
FilmFour is a British film production company and former television channel associated with Channel 4, known for backing distinctive independent and arthouse films.
-
B.
FilmScene cinema
FilmScene cinema is an independent, nonprofit movie theater and film arts organization known for showcasing arthouse, foreign, and documentary films in Iowa City, Iowa.
-
C.
VideoFilmes
VideoFilmes is a Brazilian film production company known for its work on acclaimed art-house and independent films.
-
D.
Vintry
Vintry is one of the historic wards of the City of London, traditionally associated with the wine trade along the River Thames.
-
E.
Flims
Flims is a Swiss alpine resort village in the canton of Graubünden, known for its skiing, hiking, and scenic mountain landscapes.
- 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc1fd5d48190a56981cee95ebd69 |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:50 p.m.