Triple
T8440050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chernihiv region |
E199327
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Kozelets |
E544214
|
NE 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: Kozelets | Statement: [Chernihiv region, containsTown, Kozelets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kozelets Context triple: [Chernihiv region, containsTown, Kozelets]
-
A.
Kozelets
chosen
Kozelets is an urban-type settlement in northern Ukraine, historically known as a local administrative and trading center.
-
B.
Krasny Kut
Krasny Kut is a small town in southwestern Russia known as an administrative and agricultural center within the Saratov region.
-
C.
Kožlany
Kožlany is a small town in the Czech Republic best known as the birthplace of former Czechoslovak president Edvard Beneš.
-
D.
Koshice
Košice is the second-largest city in Slovakia, known for its well-preserved medieval old town and status as an important cultural and economic center in the country.
-
E.
Kozármisleny
Kozármisleny is a small town in southern Hungary, near Pécs, known for its growing residential character and local sports culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe13708988190a534e38d8254c9bd |
completed | March 31, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1d9140b48190ad0c493948a3de5e |
completed | April 2, 2026, 7:41 a.m. |
Created at: March 30, 2026, 6:08 p.m.