Triple

T9499351
Position Surface form Disambiguated ID Type / Status
Subject 81-717/714 series E229094 entity
Predicate usedIn P98 FINISHED
Object Sofia Metro E165788 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: Sofia Metro | Statement: [81-717/714 series, usedIn, Sofia Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sofia Metro
Context triple: [81-717/714 series, usedIn, Sofia Metro]
  • A. Sofia Metro chosen
    Sofia Metro is the rapid transit system serving Bulgaria’s capital city, providing high-capacity urban rail transport across Sofia and its metropolitan area.
  • B. Bucharest Metro
    The Bucharest Metro is the rapid transit system serving Romania’s capital city, providing high-capacity urban rail transport across Bucharest.
  • C. Saint Petersburg Metro
    The Saint Petersburg Metro is a major rapid transit system in Saint Petersburg, Russia, renowned for its deep underground stations and ornate, palace-like architecture.
  • D. Novosibirsk Metro
    Novosibirsk Metro is a rapid transit system in Novosibirsk, Russia, serving as a key component of the city's public transportation network with several lines and stations across the urban area.
  • E. Moscow Metro
    The Moscow Metro is a major rapid transit system in Moscow renowned for its extensive network, high passenger capacity, and ornately decorated stations often likened to underground palaces.
  • 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_69ca84753660819098e8d416e89e26ae completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd983a94c48190a7ddf95a953c4ecc completed April 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d13a0a5ec881908bb1643d2bea2c9f completed April 4, 2026, 4:19 p.m.
Created at: March 30, 2026, 7:56 p.m.