Triple
T12728451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korchevskoy Uyezd |
E304168
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Korcheva
Korcheva was a historical town in the Tver region of Russia that served as an administrative center before being submerged by the Ivankovo Reservoir in the 1930s.
|
E1001657
|
NE FINISHED |
How this triple was built (4 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: Korcheva | Statement: [Korchevskoy Uyezd, capital, Korcheva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Korcheva Context triple: [Korchevskoy Uyezd, capital, Korcheva]
-
A.
Korotkova
Korotkova is the family name of Kira Muratova, the acclaimed Soviet and Ukrainian film director and screenwriter.
-
B.
Tarasova
Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
-
C.
Nikolayeva
Nikolayeva is a Russian surname most notably associated with the acclaimed Soviet pianist and composer Tatiana Nikolayeva.
-
D.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
E.
Kastrychnitskaya
Kastrychnitskaya is a central Minsk Metro station known for serving the heart of Belarus’s capital near key administrative and cultural landmarks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Korcheva Triple: [Korchevskoy Uyezd, capital, Korcheva]
Generated description
Korcheva was a historical town in the Tver region of Russia that served as an administrative center before being submerged by the Ivankovo Reservoir in the 1930s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Korcheva Target entity description: Korcheva was a historical town in the Tver region of Russia that served as an administrative center before being submerged by the Ivankovo Reservoir in the 1930s.
-
A.
Korotkova
Korotkova is the family name of Kira Muratova, the acclaimed Soviet and Ukrainian film director and screenwriter.
-
B.
Tarasova
Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
-
C.
Nikolayeva
Nikolayeva is a Russian surname most notably associated with the acclaimed Soviet pianist and composer Tatiana Nikolayeva.
-
D.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
E.
Kastrychnitskaya
Kastrychnitskaya is a central Minsk Metro station known for serving the heart of Belarus’s capital near key administrative and cultural landmarks.
- F. None of above. chosen
Provenance (5 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d964172490819080cd022ff8290b6e |
completed | April 10, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684e7dec08190b522a8f3bfde6fe2 |
completed | May 2, 2026, 11:12 p.m. |
| NEDg | Description generation | batch_69f685dac5cc8190b4bc2d81186c9266 |
completed | May 2, 2026, 11:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6869156048190b548ecd04561deb8 |
completed | May 2, 2026, 11:19 p.m. |
Created at: April 9, 2026, 5:25 p.m.