Triple

T15551248
Position Surface form Disambiguated ID Type / Status
Subject Bikinsky District E370748 entity
Predicate hasAdministrativeCenter P1474 FINISHED
Object Bikin E348041 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: Bikin | Statement: [Bikinsky District, hasAdministrativeCenter, Bikin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bikin
Context triple: [Bikinsky District, hasAdministrativeCenter, Bikin]
  • A. Bikin chosen
    Bikin is a town in Khabarovsk Krai in Russia, known as a local administrative and transport center near the Bikin River in the Russian Far East.
  • B. Bikel
    Bikel is a surname most notably associated with Theodore Bikel, an Austrian-American actor, folk singer, and civil rights activist.
  • C. Makin
    Makin is a small atoll in the northern Gilbert Islands of Kiribati, known for its traditional Micronesian culture and World War II history.
  • D. Bikfaya
    Bikfaya is a historic mountain town in Lebanon known for its cool climate, pine forests, and traditional Lebanese architecture.
  • E. Bicqlo
    Bicqlo is a large retail store in Shinjuku, Tokyo, created as a collaboration between electronics retailer Bic Camera and clothing brand Uniqlo.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a9551288190a583e8291c35f521 completed April 16, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff4560008c81908ebd278c3dc45045 completed May 9, 2026, 2:32 p.m.
Created at: April 10, 2026, 4:08 a.m.