Triple
T33929480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Semyen |
E869846
|
entity |
| Predicate | hasTransliterationRelation |
P182221
|
FINISHED |
| Object | Семён |
—
|
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: Семён | Statement: [Semyen, hasTransliterationRelation, Семён]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransliterationRelation Context triple: [Semyen, hasTransliterationRelation, Семён]
-
A.
transliterationRelation
chosen
Indicates that one textual form is a transliteration of another, preserving pronunciation across different writing systems.
-
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.
usesTransliteration
Indicates that one entity represents another by converting its script or characters into a different writing system according to a systematic transliteration scheme.
- 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_69f3499a59788190bff762a891471b31 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff5285ed74819097e6e2a9084a079a |
completed | May 9, 2026, 3:28 p.m. |
| PD | Predicate disambiguation | batch_69ff51fbe28881908ac8417dff9db81a |
completed | May 9, 2026, 3:25 p.m. |
Created at: May 1, 2026, 1:49 a.m.