Triple
T17425941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berezhany Raion |
E423736
|
entity |
| Predicate | hadAdministrativeFunction |
P84156
|
FINISHED |
| Object | local government |
—
|
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: local government | Statement: [Berezhany Raion, hadAdministrativeFunction, local government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadAdministrativeFunction Context triple: [Berezhany Raion, hadAdministrativeFunction, local government]
-
A.
hadJudicialFunction
Indicates that an entity exercised or was assigned an official judicial role, authority, or responsibility in relation to another entity or context.
-
B.
hasAdministrativeSignificance
chosen
Indicates that something holds official importance, authority, or relevance within an administrative or governmental context.
-
C.
hadStaffRole
Indicates that an entity served in a specific staff role or position for another entity during some period.
-
D.
administeredAs
Indicates that one entity is given or applied to another entity as a treatment, dose, or intervention.
-
E.
administrativelyIn
Indicates that one entity is located within or belongs to the jurisdiction or governance area of another entity for administrative purposes.
- 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e448fbfda88190be1c001d64289bf7 |
completed | April 19, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69e3b030eac481909b8402719cc3102e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.