Triple
T19948494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sabrina Fair |
E479490
|
entity |
| Predicate | originalProductionLanguage |
P105967
|
FINISHED |
| Object | English |
—
|
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 | Statement: [Sabrina Fair, originalProductionLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalProductionLanguage Context triple: [Sabrina Fair, originalProductionLanguage, English]
-
A.
originalLanguageOfFilmOrTVShow
Indicates the language in which a film or TV show was originally produced and released.
-
B.
primaryFilmingLanguage
Indicates the main language in which a film or audiovisual work was originally filmed or recorded.
-
C.
originalLanguageCountry
Indicates the country where a work’s original language is primarily spoken or officially used.
-
D.
originalTitleLanguage
Indicates the language in which a work’s original title was written or expressed.
-
E.
originalProductionLanguageContext
chosen
Indicates the language in which a work was originally produced or created, providing contextual information about its initial linguistic setting.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a69ae388190918e862012f9fe39 |
completed | April 20, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69e537f47c508190853c4e009c6b5566 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:54 p.m.