Triple
T6352039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grozny railway station |
E142894
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Gudermes |
E145130
|
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: Gudermes | Statement: [Grozny railway station, connectsTo, Gudermes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gudermes Context triple: [Grozny railway station, connectsTo, Gudermes]
-
A.
Gudermes
chosen
Gudermes is a town in the Chechen Republic of Russia that serves as an important regional transport and administrative center.
-
B.
Karachayevsk
Karachayevsk is a town in southwestern Russia located in the North Caucasus region, serving as one of the main urban centers of the Karachay-Cherkess Republic.
-
C.
Kasimov
Kasimov is a historic town in central Russia known for its Tatar heritage, medieval architecture, and location on the Oka River.
-
D.
Elizavetgrad
Elizavetgrad was the former name of the city now known as Kropyvnytskyi, a regional center in central Ukraine that was part of the Russian Empire at the time of Grigory Zinoviev’s birth.
-
E.
Dimitrovgrad
Dimitrovgrad is a major industrial and scientific city in Russia, known especially for its nuclear research facilities and machine-building industries.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067dd3c74819085a164b750094c46 |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c604546ed08190bb1c89bc5461f5cd |
completed | March 27, 2026, 4:15 a.m. |
Created at: March 22, 2026, 4:31 p.m.