Triple
T16135983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dzyarzhynsk |
E391527
|
entity |
| Predicate | isNear |
P350
|
FINISHED |
| Object | Dzyarzhynskaya Hara |
E72761
|
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: Dzyarzhynskaya Hara | Statement: [Dzyarzhynsk, isNear, Dzyarzhynskaya Hara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dzyarzhynskaya Hara Context triple: [Dzyarzhynsk, isNear, Dzyarzhynskaya Hara]
-
A.
Dzyarzhynskaya Hara
chosen
Dzyarzhynskaya Hara is the tallest hill in Belarus, known as the country’s highest natural elevation.
-
B.
Khreshchatyi Yar
Khreshchatyi Yar is the historic ravine area in central Kyiv whose name gave rise to the city’s main thoroughfare, Khreshchatyk Street.
-
C.
Moliukhov Bugor
Moliukhov Bugor is an archaeological site associated with the prehistoric Sredny Stog culture of the Pontic–Caspian steppe region.
-
D.
Kamennaya Gorka
Kamennaya Gorka is a metro station in Minsk, Belarus, serving as one of the western termini of the Minsk Metro system.
-
E.
Zhmerynka
Zhmerynka is a city in central Ukraine known as an important regional railway junction and administrative 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21a05148c8190bc2b98217fda23cc |
completed | April 17, 2026, 11:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7a304348190bf471f2b9279b806 |
completed | May 10, 2026, 3:12 a.m. |
Created at: April 10, 2026, 5:01 a.m.