Triple

T21073412
Position Surface form Disambiguated ID Type / Status
Subject Aileen Mioko Smith E519167 entity
Predicate notableWork P4 FINISHED
Object Minamata 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: Minamata | Statement: [Aileen Mioko Smith, notableWork, Minamata]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Minamata
Context triple: [Aileen Mioko Smith, notableWork, Minamata]
  • A. Minamata chosen
    Minamata is a powerful photo-essay and book by W. Eugene Smith documenting the devastating effects of industrial mercury poisoning on a Japanese fishing community.
  • B. Minamata
    Minamata is a coastal Japanese city historically known for the devastating industrial mercury poisoning disaster that led to the identification of Minamata disease.
  • C. Minamibōsō
    Minamibōsō is a coastal city in Chiba Prefecture, Japan, known for its scenic Pacific shoreline, mild climate, and agricultural and fishing industries.
  • D. Toyokan
    Toyokan is a gallery building of the Tokyo National Museum that primarily showcases Asian art and archaeological artifacts from regions outside Japan.
  • E. Minamichita
    Minamichita is a coastal town in central Japan known for its beaches, hot springs, and seafood on the southern tip of the Chita Peninsula in Aichi Prefecture.
  • 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_69e0b506e59c8190849b71ed07929215 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e702d491a08190a9f5f28c0b72d38c completed April 21, 2026, 4:53 a.m.
Created at: April 16, 2026, 2:47 p.m.