Triple

T4674192
Position Surface form Disambiguated ID Type / Status
Subject Philipp Franz von Siebold E103637 entity
Predicate workLocation P7 FINISHED
Object Dejima E217744 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: Dejima | Statement: [Philipp Franz von Siebold, workLocation, Dejima]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dejima
Context triple: [Philipp Franz von Siebold, workLocation, Dejima]
  • A. Dejima chosen
    Dejima was a small artificial island in Nagasaki Bay that served as the Dutch trading post and Japan’s primary window to the Western world during its period of national isolation (sakoku).
  • B. Kamitsumaki
    Kamitsumaki is the first volume of the ancient Japanese chronicle Kojiki, focusing on Shinto creation myths and the age of the gods.
  • C. Shinsekai
    Shinsekai is a retro entertainment district in Osaka, Japan, known for its nostalgic Showa-era atmosphere, street food, and neon-lit nightlife.
  • D. Takamikura
    Takamikura is the ornate imperial throne used in Kyoto for the enthronement ceremonies of Japanese emperors.
  • E. Takadanobaba
    Takadanobaba is a lively Tokyo neighborhood known for its student population, affordable eateries, and strong connections to nearby universities like Waseda.
  • 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_69bd43dda32c8190938b37744ca270fc completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd635326808190bd6909117aca1208 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69be039896448190bdfd3a6cb76a8fbe completed March 21, 2026, 2:34 a.m.
Created at: March 20, 2026, 1:15 p.m.