Triple
T34884610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Сёмин |
E1006108
|
entity |
| Predicate | распространённость |
P22713
|
FINISHED |
| Object | распространённая в России фамилия |
—
|
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: распространённая в России фамилия | Statement: [Сёмин, распространённость, распространённая в России фамилия]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: распространённость Context triple: [Сёмин, распространённость, распространённая в России фамилия]
-
A.
commonness
Indicates how frequently or typically something occurs or is found relative to other things.
-
B.
prevalentIn
chosen
Indicates that something occurs frequently or is commonly found within a particular context, group, or environment.
-
C.
spreadingStatus
Indicates the current state or progression of how something is spreading or being disseminated (e.g., whether and how it is expanding, stable, or declining).
-
D.
hasPrevalence
Indicates that something occurs or exists at a certain frequency, rate, or proportion within a defined population, group, or context.
-
E.
isWidelyUsed
Indicates that something is commonly or extensively utilized across many contexts, users, or situations.
- 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_69f76dbedb288190afe5780710847410 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782f4f10081908f97f6d0d2dbeec7 |
completed | May 3, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69f780ff71cc8190a67e71076fbad81a |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 3, 2026, 4 p.m.