Triple

T17333128
Position Surface form Disambiguated ID Type / Status
Subject Kingiseppsky District E420863 entity
Predicate hasUrbanCenter P2106 FINISHED
Object Kingisepp 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: Kingisepp | Statement: [Kingiseppsky District, hasUrbanCenter, Kingisepp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kingisepp
Context triple: [Kingiseppsky District, hasUrbanCenter, Kingisepp]
  • A. Kingisepp chosen
    Kingisepp is a town in northwestern Russia near the Estonian border, known for its industrial base and historical roots dating back to the 14th century.
  • B. Makov
    Makov is a Slovak village and popular mountain resort known as a key gateway for hiking and winter sports in the Javorníky Mountains.
  • C. Muroran
    Muroran is an industrial port city in southern Hokkaido, Japan, known for its steel industry and scenic coastal landscapes.
  • D. Zikhron Ya’akov
    Zikhron Ya’akov is a historic town in northern Israel known for its early Zionist agricultural settlement, wineries, and scenic location overlooking the Mediterranean.
  • E. Lichtenrade
    Lichtenrade is a southern residential locality of Berlin known for its village-like character, green spaces, and proximity to the city’s outskirts.
  • 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_69d889d3adc881909319f1edb8d2a956 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a0fa57881909071fd395b3d46c4 completed April 19, 2026, 2:12 a.m.
Created at: April 10, 2026, 5:43 a.m.