Triple
T36347271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Старый Шивелуч |
E895097
|
entity |
| Predicate | отношениеКМолодомуШивелучу |
P152948
|
FINISHED |
| Object | служит основанием для формирования Молодого Шивелуча |
—
|
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: служит основанием для формирования Молодого Шивелуча | Statement: [Старый Шивелуч, отношениеКМолодомуШивелучу, служит основанием для формирования Молодого Шивелуча]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: отношениеКМолодомуШивелучу Context triple: [Старый Шивелуч, отношениеКМолодомуШивелучу, служит основанием для формирования Молодого Шивелуча]
-
A.
повʼязанеЗ
chosen
Indicates a general association or connection between one entity and another.
-
B.
relationshipToPavelVlasov
Indicates the nature or type of relationship an entity has with Pavel Vlasov.
-
C.
relationshipToMakarDevushkin
Indicates the nature or type of relationship an entity has with Makar Devushkin.
-
D.
hasRelationshipTypeWithAglayaIvanovna
Indicates that an entity has a specific type of relationship or connection with Aglaya Ivanovna.
-
E.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
- 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_69f76e4f437c8190a1af3ea2564f41f5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7bb3ff1b08190802b1063d55d3923 |
completed | May 3, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a611a081908dd6aec1df3f4d7f |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:09 p.m.