Triple

T9101054
Position Surface form Disambiguated ID Type / Status
Subject Prospekt Vernadskogo E218152 entity
Predicate servesDistrict P82 FINISHED
Object Ramenki District E285361 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: Ramenki District | Statement: [Prospekt Vernadskogo, servesDistrict, Ramenki District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ramenki District
Context triple: [Prospekt Vernadskogo, servesDistrict, Ramenki District]
  • A. Ramenki District chosen
    Ramenki District is a residential and educational area in western Moscow, known for its universities, green spaces, and proximity to major city transport routes.
  • B. Gusu District
    Gusu District is the central urban district of Suzhou, China, known for its historic canals, classical gardens, and well-preserved ancient cityscape.
  • C. Inukami District
    Inukami District is a rural administrative district in Shiga Prefecture, Japan, known for its small towns and scenic countryside.
  • D. Govuro District
    Govuro District is an administrative district located in Inhambane Province in southern Mozambique.
  • E. Bikinsky District
    Bikinsky District is an administrative and municipal district in Khabarovsk Krai in Russia, known for its rural localities and position in the Russian Far East.
  • 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc9711babc8190a336812dd08d9c73 completed April 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05449101481908c71475acf59b33c completed April 3, 2026, 11:59 p.m.
Created at: March 30, 2026, 7:15 p.m.