Triple

T16239784
Position Surface form Disambiguated ID Type / Status
Subject Maibara E394210 entity
Predicate borders P224 FINISHED
Object Hikone 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: Hikone | Statement: [Maibara, borders, Hikone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hikone
Context triple: [Maibara, borders, Hikone]
  • A. Hikone chosen
    Hikone is a historic city in Shiga Prefecture, Japan, best known for its well-preserved Hikone Castle overlooking Lake Biwa.
  • B. Kitanagoya
    Kitanagoya is a city in central Japan known as a residential and commercial suburb within the Nagoya metropolitan area.
  • C. Kofu
    Kofu is the capital city of Yamanashi Prefecture in central Japan, known for its surrounding mountains, hot springs, and proximity to the Fuji Five Lakes region.
  • D. Izumisano
    Izumisano is a coastal city in Osaka Prefecture, Japan, known as the mainland gateway to Kansai International Airport and a hub for regional commerce and travel.
  • E. Nagahama
    Nagahama is a historic lakeside city in central Japan known for its preserved Edo-period streets, Nagahama Castle, and scenic location on the northeastern shore of Lake Biwa.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2455d5270819090171d4207223a28 completed April 17, 2026, 2:36 p.m.
Created at: April 10, 2026, 5:04 a.m.