Triple
T18473633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kursk Oblast |
E451366
|
entity |
| Predicate | hasCityStatus |
P3422
|
FINISHED |
| Object | Zheleznogorsk |
—
|
NE NERFINISHED |
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: Zheleznogorsk | Statement: [Kursk Oblast, hasCityStatus, Zheleznogorsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zheleznogorsk Context triple: [Kursk Oblast, hasCityStatus, Zheleznogorsk]
-
A.
Zheleznogorsk-Ilimsky
Zheleznogorsk-Ilimsky is a small industrial town in Russia known for its mining and forestry-related industries.
-
B.
Yuzhnouralsk
Yuzhnouralsk is a small industrial city in Russia’s Ural region, known for its energy and manufacturing enterprises.
-
C.
Zheleznogorsk (iron city)
chosen
Zheleznogorsk (iron city) is a Russian town known for its origins and development around iron ore mining and metallurgical industries.
-
D.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
E.
Novokuznetskaya
Novokuznetskaya is a Moscow Metro station known for its distinctive Stalinist architecture and richly decorated interiors.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e530617e48819091240d4405e53aaa |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 11:34 a.m.