Triple

T12801621
Position Surface form Disambiguated ID Type / Status
Subject JR Tōkai E306031 entity
Predicate operatesService P5884 FINISHED
Object Nozomi E306032 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: Nozomi | Statement: [JR Tōkai, operatesService, Nozomi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nozomi
Context triple: [JR Tōkai, operatesService, Nozomi]
  • A. Nozomi chosen
    Nozomi is the fastest and most premium Shinkansen (bullet train) service operating on Japan’s Tokaido and Sanyo lines, known for its high speed and frequent departures between major cities like Tokyo and Osaka.
  • B. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • C. Sanae
    Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
  • D. Naoko
    Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
  • E. Mayumi
    Mayumi is a Japanese surname borne by various individuals, including Akinobu Mayumi, and can also be used as a given name.
  • 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_69d7bdf366888190a8cccb982606889c completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96e7d3f5c8190bf01bef5d263ca26 completed April 10, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69b9418b08190a61ee4ec4283767c completed May 3, 2026, 12:49 a.m.
Created at: April 9, 2026, 5:30 p.m.