Triple
T1385463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monsieur Ibrahim |
E29833
|
entity |
| Predicate | hasLanguageSubtitle |
P9278
|
FINISHED |
| Object | English subtitles (for international releases) |
—
|
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: English subtitles (for international releases) | Statement: [Monsieur Ibrahim, hasLanguageSubtitle, English subtitles (for international releases)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageSubtitle Context triple: [Monsieur Ibrahim, hasLanguageSubtitle, English subtitles (for international releases)]
-
A.
hasIntertitlesLanguage
Indicates that the intertitles of a film or audiovisual work are presented in a specified language.
-
B.
hasLanguageOn
chosen
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
C.
hasSubLanguage
Indicates that one language is a subset, variant, or specialized form of another language.
-
D.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
E.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c339f3d481909c04b14129899945 |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.