Triple
T6792535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krasnoyarsk Krai |
E155967
|
entity |
| Predicate | federalSubjectCode |
P58137
|
FINISHED |
| Object | 24 |
—
|
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: 24 | Statement: [Krasnoyarsk Krai, federalSubjectCode, 24]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: federalSubjectCode Context triple: [Krasnoyarsk Krai, federalSubjectCode, 24]
-
A.
federalSubject
Indicates that one entity is a federal subject (a primary administrative or constituent unit) of the other entity, typically a sovereign state or federation.
-
B.
federalGovernmentCode
chosen
Indicates that an entity is associated with, identified by, or governed under a specific code or classification defined by the federal government.
-
C.
containsFederalSubject
Indicates that one administrative or territorial entity includes a specific federal subject within its jurisdiction or boundaries.
-
D.
federalSubjectCount
Indicates the number of federal subjects (administrative units within a federation) associated with or contained by an entity.
-
E.
federalGovernmentSubdivision
Indicates that one governmental entity is an administrative or political subdivision within a larger federal government structure.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2ae4d1c819089ac6b3abf11a341 |
completed | March 27, 2026, 6:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d0979ce0819094678896da4e3169 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:15 p.m.