Triple
T7217203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moksha language |
E149565
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Tambov Oblast |
E444723
|
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: Tambov Oblast | Statement: [Moksha language, region, Tambov Oblast]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tambov Oblast Context triple: [Moksha language, region, Tambov Oblast]
-
A.
Tambov Oblast
chosen
Tambov Oblast is a federal subject of central Russia known for its fertile agricultural lands and location along the middle reaches of the Don River.
-
B.
Ryazan Oblast
Ryazan Oblast is a federal subject of central Russia known for its historic cities, agricultural landscapes, and location along the Oka River southeast of Moscow.
-
C.
Voronezh Oblast
Voronezh Oblast is a federal subject of Russia in the country’s southwest, known for its administrative center Voronezh and its role as an important agricultural and industrial region.
-
D.
Lipetsk Oblast
Lipetsk Oblast is a federal subject of western Russia known for its industrial centers, agricultural production, and administrative capital, the city of Lipetsk.
-
E.
Penza Oblast
Penza Oblast is a federal subject of central Russia known for its agricultural economy, mixed forests, and role as a regional industrial and cultural center.
- 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_69c687eca814819095abb52316b1af80 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e99170d88190b1aef326a7d81134 |
completed | March 27, 2026, 8:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f018324bf88190bcd2bf168b1065d3 |
completed | April 28, 2026, 2:15 a.m. |
Created at: March 27, 2026, 2:53 p.m.