Triple
T16135989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dzyarzhynsk |
E391527
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Dzerzhinsk |
E290966
|
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: Dzerzhinsk | Statement: [Dzyarzhynsk, hasAlternativeName, Dzerzhinsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dzerzhinsk Context triple: [Dzyarzhynsk, hasAlternativeName, Dzerzhinsk]
-
A.
Dzerzhinsk
chosen
Dzerzhinsk is a major industrial city in western Russia known for its large chemical manufacturing sector and associated environmental issues.
-
B.
Dzyarzhynsk
Dzyarzhynsk is a town in Belarus known for its proximity to Dzyarzhynskaya Hara, the country’s highest point.
-
C.
Mahilyowskaya
Mahilyowskaya is a metro station on the Minsk Metro system in Minsk, Belarus.
-
D.
Novozybkov
Novozybkov is a town in western Russia known as a local administrative and economic center near the borders with Belarus and Ukraine.
-
E.
Klimowitschi
Klimowitschi is a town in Belarus known in part for its international municipal partnership with Werder (Havel) in Germany.
- 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_69fff2b39bbc8190a2cb77a3f0a329fd |
completed | May 10, 2026, 2:51 a.m. |
Created at: April 10, 2026, 5:01 a.m.