Triple
T9092263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novocherkassk |
E217917
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Don region |
E229102
|
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: Don region | Statement: [Novocherkassk, region, Don region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don region Context triple: [Novocherkassk, region, Don region]
-
A.
Don region
chosen
The Don region is a historical area in southern Russia centered around the Don River, traditionally associated with the homeland of the Don Cossacks.
-
B.
Choiseul region
The Choiseul region is an area of the Solomon Islands centered on Choiseul Island, known for its indigenous communities and use of Northwest Solomonic languages.
-
C.
Maekel Region
Maekel Region is a central administrative region of Eritrea that includes the nation’s capital, Asmara, and serves as its political and economic hub.
-
D.
Waldeck region
The Waldeck region is a historical area in central Germany, known for its former status as a small principality and later Free State within the German territories.
-
E.
Diffa Region
Diffa Region is a sparsely populated, conflict-affected administrative region in southeastern Niger bordering Nigeria and Chad, known for insecurity linked to Boko Haram and Islamic State–affiliated insurgent groups.
- 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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc96b18b24819097b525ddad3a85c0 |
completed | April 1, 2026, 3:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d017f85c908190a4e90c22a75348b5 |
completed | April 3, 2026, 7:41 p.m. |
Created at: March 30, 2026, 7:14 p.m.