Triple
T14183824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | youth jury of the Berlin International Film Festival |
E351523
|
entity |
| Predicate | targetAudienceOfFilms |
P113127
|
FINISHED |
| Object | young audiences |
—
|
LITERAL 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: young audiences | Statement: [youth jury of the Berlin International Film Festival, targetAudienceOfFilms, young audiences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetAudienceOfFilms Context triple: [youth jury of the Berlin International Film Festival, targetAudienceOfFilms, young audiences]
-
A.
sourceFilmGenre
Indicates that a film is classified as belonging to a particular genre.
-
B.
keyGenreFilm
Indicates that a particular genre is the primary or defining genre associated with a given film.
-
C.
featuredInFilmGenre
Indicates that an entity (such as a film, character, or work) appears in or is associated with a specific film genre.
-
D.
distributedFilmGenre
Indicates that an entity (such as a company or distributor) distributed a film belonging to a particular genre.
-
E.
subjectOfFilm
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
- F. None of above. chosen
Provenance (4 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61cc0a848190b660095972b1223b |
completed | April 14, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69de05baed64819096590e5618a3a8ed |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239a02e881909b0e2679487e4ab2 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 10, 2026, 1:03 a.m.