Triple

T19557597
Position Surface form Disambiguated ID Type / Status
Subject Lensky District E489356 entity
Predicate hasCapital P204 FINISHED
Object Lensk 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: Lensk | Statement: [Lensky District, hasCapital, Lensk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lensk
Context triple: [Lensky District, hasCapital, Lensk]
  • A. Lensk chosen
    Lensk is a small industrial town in the Sakha Republic of Russia, known for its role in regional river transport and nearby diamond mining activities.
  • B. Luts’k
    Luts’k is a historic city in northwestern Ukraine, known as the administrative center of Volyn Oblast and for its well-preserved medieval castle.
  • C. Solikamsk
    Solikamsk is a historic industrial city in Russia known for its major salt and chemical industries and its location in the northern part of Perm Krai.
  • D. Kirovabad
    Kirovabad was the Soviet-era name of the Azerbaijani city now known as Ganja, an important industrial and cultural center in western Azerbaijan.
  • E. Neftekamsk
    Neftekamsk is an industrial city in the Republic of Bashkortostan, Russia, known for its oil-related industries and vehicle manufacturing.
  • 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63d34d1bc81908e5e10f069655866 completed April 20, 2026, 2:50 p.m.
Created at: April 10, 2026, 1:42 p.m.