Triple
T2581694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tver Oblast |
E57105
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Konakovo |
E332297
|
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: Konakovo | Statement: [Tver Oblast, contains, Konakovo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Konakovo Context triple: [Tver Oblast, contains, Konakovo]
-
A.
Konakovo
chosen
Konakovo is a town in Tver Oblast, Russia, situated on the Volga River and known for its power station and riverside recreation.
-
B.
Skovorodino
Skovorodino is a small town in Russia’s Far Eastern Amur Oblast, known historically as a railway junction on the Trans-Siberian Railway.
-
C.
Krasnogorsk
Krasnogorsk is a city in western Russia that serves as an important administrative and residential center just outside Moscow.
-
D.
Orekhovo
Orekhovo is a Moscow Metro station on the Zamoskvoretskaya Line serving the Orekhovo-Borisovo district in southern Moscow.
-
E.
Ramenskoye
Ramenskoye is a town in Moscow Oblast, Russia, located southeast of Moscow and known for its industrial base and proximity to major Moscow airports.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3c843bc8190837cea3441bf3ca1 |
completed | March 7, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b276b188248190b015eec2b83bc20b |
completed | March 12, 2026, 8:17 a.m. |
Created at: March 6, 2026, 9:49 p.m.