Triple
T29303193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Semenivka |
E743015
|
entity |
| Predicate | administrativeReformEffect |
P146599
|
FINISHED |
| Object | Semenivka Raion abolished in 2020 |
—
|
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: Semenivka Raion abolished in 2020 | Statement: [Semenivka, administrativeReformEffect, Semenivka Raion abolished in 2020]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeReformEffect Context triple: [Semenivka, administrativeReformEffect, Semenivka Raion abolished in 2020]
-
A.
typeOfReforms
Indicates the specific kinds or categories of reforms associated with an entity or situation.
-
B.
implementedReformsIn
Indicates that an entity (typically a person, organization, or government) carried out or put into effect specific reforms within a particular context, domain, or location.
-
C.
consequenceOfReforms
chosen
Indicates that something occurs as a result of, or is caused by, a set of reforms.
-
D.
goalOfReforms
Indicates that a reform or set of reforms is undertaken with the aim or intended objective of achieving a particular outcome.
-
E.
reform
Indicates bringing about significant changes to an existing system, practice, or entity in order to improve or correct it.
- 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_69f09123ed9881909f351f7541933f5e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: April 28, 2026, 1:11 p.m.