Triple
T10918852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hårga |
E257890
|
entity |
| Predicate | filmSettingType |
P52439
|
FINISHED |
| Object | rural Sweden |
—
|
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: rural Sweden | Statement: [Hårga, filmSettingType, rural Sweden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmSettingType Context triple: [Hårga, filmSettingType, rural Sweden]
-
A.
filmSetting
chosen
Indicates the place, time, or environment in which the events of a film are set or take place.
-
B.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
C.
filmType
Indicates the specific category or genre that a film belongs to.
-
D.
filmSceneType
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
-
E.
filmProductionType
Indicates the specific kind or category of production under which a film was made (e.g., feature, short, documentary, TV movie).
- F. None of above.
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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d77080317881909fc50ac3576cefa8 |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.