Triple

T12659035
Position Surface form Disambiguated ID Type / Status
Subject Kurseong E302365 entity
Predicate governingBody P46 FINISHED
Object Kurseong Municipality E302365 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: Kurseong Municipality | Statement: [Kurseong, governingBody, Kurseong Municipality]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kurseong Municipality
Context triple: [Kurseong, governingBody, Kurseong Municipality]
  • A. Kurseong chosen
    Kurseong is a small hill town in the Darjeeling district of northern West Bengal, India, known for its tea gardens, cool climate, and views of the Eastern Himalayas.
  • B. Geumwang-eup
    Geumwang-eup is a town-level administrative division in Eumseong County, located in North Chungcheong Province, South Korea.
  • C. Muan County
    Muan County is a rural administrative region in South Jeolla Province, South Korea, known for its role as the provincial capital and its agricultural and coastal landscapes.
  • D. Seocheon County
    Seocheon County is a coastal administrative region in South Chungcheong Province, South Korea, known for its tidal flats, fishing industry, and ecological wetlands.
  • E. Dalseong County
    Dalseong County is a largely rural administrative district on the outskirts of Daegu in South Korea, known for its natural scenery, agricultural areas, and growing suburban developments.
  • 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_69d7bded71a88190bb76e2413af9ea66 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d961636db8819099c438b24bcfd866 completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f66885e44c8190a650301b0e86d0f4 completed May 2, 2026, 9:11 p.m.
Created at: April 9, 2026, 5:19 p.m.