Triple

T12142809
Position Surface form Disambiguated ID Type / Status
Subject Ebisu Station E289230 entity
Predicate fareSystem P395 FINISHED
Object PASMO E132941 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: PASMO | Statement: [Ebisu Station, fareSystem, PASMO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PASMO
Context triple: [Ebisu Station, fareSystem, PASMO]
  • A. PASMO chosen
    PASMO is a rechargeable contactless smart card widely used for public transportation and electronic payments across the Tokyo metropolitan area.
  • B. Suica
    Suica is a rechargeable contactless smart card issued by JR East that is widely used for train fares and electronic payments across Japan.
  • C. Keihan fare system
    The Keihan fare system is the ticketing and pricing structure used across the Keihan Electric Railway network in the Kansai region of Japan.
  • D. Kitaca
    Kitaca is a rechargeable contactless smart card used primarily for public transportation and electronic payments in Japan’s Hokkaido region.
  • E. Tokyo Metro
    Tokyo Metro is one of the two main rapid transit operators in Tokyo, running an extensive network of subway lines that serve the city’s central and metropolitan areas.
  • 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_69d6ab4c6710819097a9d228382dde43 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915a9838081909622cc14df2a2582 completed April 10, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a7e24ac819083e85fb8edb2ed2c completed May 2, 2026, 2:30 p.m.
Created at: April 8, 2026, 9:49 p.m.