Triple
T4340945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Filmstaden Sergel |
E97576
|
entity |
| Predicate | languageOfMostScreenings |
P40556
|
FINISHED |
| Object | Swedish subtitles |
—
|
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: Swedish subtitles | Statement: [Filmstaden Sergel, languageOfMostScreenings, Swedish subtitles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfMostScreenings Context triple: [Filmstaden Sergel, languageOfMostScreenings, Swedish subtitles]
-
A.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
B.
languageSpokenOnScreen
Indicates that a particular language is used in spoken dialogue or audible communication within an on-screen work (such as a film, show, or video).
-
C.
majorityLanguageOf
Indicates that a given language is the primary or most widely spoken language within a specified group, region, or entity.
-
D.
dominantMediaLanguage
chosen
Indicates that one language is the primary or most prevalent medium of communication used in a given media context or outlet.
-
E.
languageOfPrimaryBroadcasts
Indicates the language in which an entity’s primary broadcasts are delivered.
- 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_69b3454662a481908fbcd0bbfaa3a0a4 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35170a6648190a15ffb21640ee478 |
completed | March 12, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69b34f4fe1c481908d6d66e15697c04b |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:14 p.m.