Triple

T15105956
Position Surface form Disambiguated ID Type / Status
Subject European route E40 E360786 entity
Predicate connectsTo P845 FINISHED
Object Karaganda E52322 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: Karaganda | Statement: [European route E40, connectsTo, Karaganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karaganda
Context triple: [European route E40, connectsTo, Karaganda]
  • A. Karaganda chosen
    Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
  • B. Kokshetau
    Kokshetau is a city in northern Kazakhstan that serves as the administrative and economic center of the surrounding Akmola Region.
  • C. Syktyvkar
    Syktyvkar is the capital city of the Komi Republic in northwestern Russia, known as an administrative, cultural, and economic center of the region.
  • D. Zhezkazgan
    Zhezkazgan is a major industrial and mining city in central Kazakhstan, known especially for its large copper deposits and metallurgical complex.
  • E. Kaspiysk
    Kaspiysk is a coastal city on the Caspian Sea in the Republic of Dagestan, Russia, known for its industrial base and strategic naval facilities.
  • 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_69d85a0491ec8190830960be8fafb994 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00588f35481909674f161bf0f3918 completed April 15, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69feb7e912ac8190bd0e0c9cdbbd0194 completed May 9, 2026, 4:28 a.m.
Created at: April 10, 2026, 3:05 a.m.