Triple
T10083150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cherkasy Oblast |
E213950
|
entity |
| Predicate | hasCityStatus |
P3422
|
FINISHED |
| Object | Smila |
E841410
|
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: Smila | Statement: [Cherkasy Oblast, hasCityStatus, Smila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Smila Context triple: [Cherkasy Oblast, hasCityStatus, Smila]
-
A.
Smila
chosen
Smila is a city in central Ukraine known as an industrial and transport hub within Cherkasy Oblast.
-
B.
Mimili
Mimili is a remote Aboriginal community in South Australia, home primarily to Pitjantjatjara people and known for its strong cultural traditions and art.
-
C.
Leka
Leka is a small island municipality in Trøndelag county, Norway, known for its distinctive geology and coastal landscape.
-
D.
Tsalka
Tsalka is a town in southern Georgia known for its ethnically diverse population and its location near the Tsalka Reservoir in the Kvemo Kartli region.
-
E.
Vimeu
Vimeu is a historical region in northern France, known for its medieval significance and as the site of the Battle of Saucourt-en-Vimeu.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd04352d081908f676444cd2d2578 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2cbcc82988190903efb78d0a4ce81 |
completed | April 5, 2026, 8:53 p.m. |
Created at: March 30, 2026, 9 p.m.