Triple
T3193170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roxana |
E66871
|
entity |
| Predicate | hasTransliterationFrom |
P5923
|
FINISHED |
| Object | Persian name "Roxane" / "Roxana" |
—
|
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: Persian name "Roxane" / "Roxana" | Statement: [Roxana, hasTransliterationFrom, Persian name "Roxane" / "Roxana"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransliterationFrom Context triple: [Roxana, hasTransliterationFrom, Persian name "Roxane" / "Roxana"]
-
A.
alternativeTransliteration
chosen
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
-
B.
transliterationLanguage
Indicates the language whose writing system is used as the target when converting text from one script to another.
-
C.
hasRomanizationOf
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
D.
standardTransliteration
Indicates that one representation of text is a transliteration of another according to a recognized standard or convention.
-
E.
hasTranslation
Indicates that one entity is a translation or translated version of another entity in a different language.
- 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_69ad8588ba18819086a10951c32ecb80 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada713bf0c81908f3143f45f63a5ad |
completed | March 8, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69ad9e04290481909092ddfbe6fdaabc |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:07 p.m.