Triple
T1108414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rita Vrataski |
E25537
|
entity |
| Predicate | originalWorkLanguage |
P5459
|
FINISHED |
| Object | Japanese (light novel) |
—
|
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: Japanese (light novel) | Statement: [Rita Vrataski, originalWorkLanguage, Japanese (light novel)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalWorkLanguage Context triple: [Rita Vrataski, originalWorkLanguage, Japanese (light novel)]
-
A.
originalTextLanguage
chosen
Indicates the language in which a text was originally written or created before any translation or adaptation.
-
B.
originalPublicationLanguageVariant
Indicates that one language is a specific variant or version of the language in which a work was originally published.
-
C.
originalLanguageContext
Indicates the language in which something was first created or expressed, providing the original linguistic context for its content or meaning.
-
D.
primaryLanguageOf
Indicates that a specified language is the main or official language used by a particular entity (such as a person, organization, or region).
-
E.
originalLanguageTitle
Indicates the title of a work as it appears in its original language of creation or publication.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e6134481909f348986a25f65c6 |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b749e2a881909ef28745a7d2d917 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.