Triple

T9499348
Position Surface form Disambiguated ID Type / Status
Subject 81-717/714 series E229094 entity
Predicate usedIn P98 FINISHED
Object Bucharest Metro E160604 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: Bucharest Metro | Statement: [81-717/714 series, usedIn, Bucharest Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bucharest Metro
Context triple: [81-717/714 series, usedIn, Bucharest Metro]
  • A. Bucharest Metro chosen
    The Bucharest Metro is the rapid transit system serving Romania’s capital city, providing high-capacity urban rail transport across Bucharest.
  • B. Bucharest tram network
    The Bucharest tram network is an extensive urban light rail system that complements the city’s metro and bus services by providing surface-level public transportation across Romania’s capital.
  • C. Sofia Metro
    Sofia Metro is the rapid transit system serving Bulgaria’s capital city, providing high-capacity urban rail transport across Sofia and its metropolitan area.
  • D. Budapest Metro
    The Budapest Metro is the rapid transit system serving Hungary’s capital, notable for including Line 1, one of the oldest electrified underground railway lines in continental Europe.
  • E. Turin Metro
    The Turin Metro is a fully automated, driverless rapid transit system serving the city of Turin, Italy.
  • 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_69d12d412a008190adc82e1e3d56d107 completed April 4, 2026, 3:24 p.m.
Created at: March 30, 2026, 7:56 p.m.