Triple
T14722661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Carlos |
E345853
|
entity |
| Predicate | translatedLanguage |
P21151
|
FINISHED |
| Object | Italian |
—
|
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: Italian | Statement: [Don Carlos, translatedLanguage, Italian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translatedLanguage Context triple: [Don Carlos, translatedLanguage, Italian]
-
A.
translationTargetLanguage
chosen
Indicates the language into which content is being or has been translated.
-
B.
translatedIn
Indicates that a work, text, or content has been rendered from its original language into another specified language or linguistic form.
-
C.
languageTranslatedFrom
Indicates that a language is the source/original language from which content has been translated into another language.
-
D.
otherLanguage
Indicates that an entity has or uses an additional language distinct from its primary or main language.
-
E.
textTranslation
Indicates a relationship where one text is rendered into another language or form while preserving its original meaning.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25d56fc8190871873ca55d49272 |
completed | April 14, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69de657e174481909da0437556334a04 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.