Triple

T9346133
Position Surface form Disambiguated ID Type / Status
Subject Fukuyama E224894 entity
Predicate hasLandmark P105 FINISHED
Object Tomonoura E634375 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: Tomonoura | Statement: [Fukuyama, hasLandmark, Tomonoura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tomonoura
Context triple: [Fukuyama, hasLandmark, Tomonoura]
  • A. Tomonoura chosen
    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.
  • B. Kamiyama
    Kamiyama is a Japanese surname borne by various individuals, including artists, athletes, and public figures.
  • C. Marunouchi
    Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
  • D. Mikasuki
    Mikasuki is a Native American language of the Muskogean family, traditionally spoken by the Miccosukee and some Seminole people in the southeastern United States.
  • E. Oyamazaki
    Oyamazaki is a town in Kyoto Prefecture, Japan, known for its historical significance and scenic location at the confluence of major rivers and transportation routes.
  • 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_69ca842993248190a79ab06968994b86 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd4f0e2ccc8190a68f1c96c0886660 completed April 1, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69dbac7a42348190a86fa5a97e3d36ca completed April 12, 2026, 2:30 p.m.
Created at: March 30, 2026, 7:41 p.m.