Triple
T2775040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samara Oblast |
E61547
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Novokuybyshevsk |
E386558
|
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: Novokuybyshevsk | Statement: [Samara Oblast, hasMajorCity, Novokuybyshevsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Novokuybyshevsk Context triple: [Samara Oblast, hasMajorCity, Novokuybyshevsk]
-
A.
Novokuybyshevsk
chosen
Novokuybyshevsk is an industrial city in Samara Oblast, Russia, known for its major oil refining and petrochemical industries.
-
B.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
-
C.
Kirovsk
Kirovsk is an industrial town in Russia’s Murmansk Oblast, known for its mining industry and location in the Khibiny Mountains on the Kola Peninsula.
-
D.
Kirovsk
Kirovsk is a small industrial town in northwestern Russia, situated near Saint Petersburg along the Neva River.
-
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 (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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd7f9570819087f1b1cb59d68586 |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd677fd8a481909455b2a02830bcc6 |
completed | March 20, 2026, 3:28 p.m. |
Created at: March 6, 2026, 9:57 p.m.