Triple

T7656543
Position Surface form Disambiguated ID Type / Status
Subject Tokyo metropolitan rail network E173398 entity
Predicate includes P1393 FINISHED
Object Tokyo Monorail E186921 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: Tokyo Monorail | Statement: [Tokyo metropolitan rail network, includes, Tokyo Monorail]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tokyo Monorail
Context triple: [Tokyo metropolitan rail network, includes, Tokyo Monorail]
  • A. Tokyo Monorail chosen
    Tokyo Monorail is an urban transit line in Tokyo that provides rapid rail service between central Tokyo and Haneda Airport.
  • B. Osaka Monorail
    Osaka Monorail is a straddle-beam monorail system in Osaka Prefecture, Japan, serving as a major urban transit line linking key suburbs, commercial areas, and transport hubs.
  • C. Tama Monorail
    Tama Monorail is a straddle-beam monorail line in Tokyo, Japan, providing urban transit service through the Tama area.
  • D. Okinawa Urban Monorail
    Okinawa Urban Monorail is an elevated rail transit system serving the city of Naha and surrounding areas on Japan’s Okinawa Island.
  • E. KL Monorail
    KL Monorail is an elevated urban rail line in Kuala Lumpur that provides rapid transit service through the city’s central and commercial districts.
  • 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_69c69955517c819085bc715b96d304d2 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7018fcbb48190a479f2effd939a8e completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c89b05846c8190b49540aeae43dd9a completed March 29, 2026, 3:22 a.m.
Created at: March 27, 2026, 3:59 p.m.