Triple

T7186272
Position Surface form Disambiguated ID Type / Status
Subject Cota E167578 entity
Predicate borderedBy P224 FINISHED
Object Tenjo E37138 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: Tenjo | Statement: [Cota, borderedBy, Tenjo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tenjo
Context triple: [Cota, borderedBy, Tenjo]
  • A. Tenjo chosen
    Tenjo is a small municipality and town in the department of Cundinamarca, Colombia, known for its rural landscapes and proximity to Bogotá.
  • B. Tenjo
    Tenjo is a district in West Java, Indonesia, known as part of the greater Bogor area on the outskirts of Jakarta.
  • C. Tokoro
    Tokoro is a coastal district of Kitami City in Hokkaido, Japan, known historically for its fishing industry and drift ice along the Sea of Okhotsk.
  • D. Tokoname
    Tokoname is a coastal city in Aichi Prefecture, Japan, historically renowned as one of the country’s Six Ancient Kilns for its distinctive ceramic and pottery production.
  • E. Tomonoura
    Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
  • 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_69c6888b5248819090499a884ee3ec39 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e8e0f2f48190a4ddf8637f556934 completed March 27, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7b950f90c81908391a96071594ea6 completed March 28, 2026, 11:19 a.m.
Created at: March 27, 2026, 2:49 p.m.