Triple

T21482426
Position Surface form Disambiguated ID Type / Status
Subject Ōdate E530026 entity
Predicate hasNeighboringMunicipality P224 FINISHED
Object Kitaakita 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: Kitaakita | Statement: [Ōdate, hasNeighboringMunicipality, Kitaakita]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kitaakita
Context triple: [Ōdate, hasNeighboringMunicipality, Kitaakita]
  • A. Kitaakita chosen
    Kitaakita is a city in northern Japan known for its mountainous landscapes, hot springs, and traditional festivals within Akita Prefecture.
  • B. Ibajay
    Ibajay is a coastal municipality in the Philippine province of Aklan known for its mangrove forest and agricultural communities.
  • C. Bikita
    Bikita is a rural district and settlement in southeastern Zimbabwe known for its lithium-rich mineral deposits and agricultural communities.
  • D. Hinatuan
    Hinatuan is a coastal municipality in the province of Surigao del Sur in the Philippines, best known for its clear blue Hinatuan Enchanted River.
  • E. Kabayan
    Kabayan is a mountainous municipality in the Philippine province of Benguet known for its rice terraces, scenic highland landscapes, and ancient mummified remains.
  • 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_69e0c45acc3881908e38d3f28964152b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9ea34c4388190adc78d209d2aafb8 completed April 23, 2026, 9:45 a.m.
Created at: April 16, 2026, 6:21 p.m.