Triple
T29924188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pripjat |
E760031
|
entity |
| Predicate | usesTransliterationSystem |
P107909
|
FINISHED |
| Object | Belarusian Latin transliteration |
—
|
NE NERFINISHED |
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: Belarusian Latin transliteration | Statement: [Pripjat, usesTransliterationSystem, Belarusian Latin transliteration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTransliterationSystem Context triple: [Pripjat, usesTransliterationSystem, Belarusian Latin transliteration]
-
A.
commonTransliterationSystem
Indicates that two or more written forms are derived using the same standardized system for converting text from one script to another.
-
B.
hasTransliterationRole
Indicates that an entity participates in a transliteration process with a specific role (e.g., source, target, or agent of transliteration).
-
C.
hasTransliterationType
Indicates the type or system of transliteration used to convert text from one writing system into another.
-
D.
hasTransliterationRule
Indicates that there exists a specific rule or mapping that defines how text in one script or writing system is systematically converted into another.
-
E.
transliterationType
chosen
Indicates the specific system or method used to convert text from one writing system into another using corresponding characters.
- 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_69f224631674819080c8d089674f9f4f |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f72921cf2c8190909bb53f78bcc890 |
completed | May 3, 2026, 10:53 a.m. |
| PD | Predicate disambiguation | batch_69f7283d8cec8190b524c144948bc4ec |
completed | May 3, 2026, 10:49 a.m. |
Created at: April 29, 2026, 6:15 p.m.