Triple

T22329983
Position Surface form Disambiguated ID Type / Status
Subject Lambertseter Line E551997 entity
Predicate usesRollingStock P5426 FINISHED
Object MX3000 trains NE NERFINISHED

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: MX3000 trains | Statement: [Lambertseter Line, usesRollingStock, MX3000 trains]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MX3000 trains
Context triple: [Lambertseter Line, usesRollingStock, MX3000 trains]
  • A. MX3000 train chosen
    The MX3000 train is a modern electric multiple unit used as the primary passenger rolling stock on the Oslo Metro system in Norway.
  • B. M300 series trains
    The M300 series trains are a modern fleet of metro trains operating on the Helsinki Metro, designed to provide efficient, high-capacity urban rail transport.
  • C. M200 series trains
    The M200 series trains are a fleet of modern electric multiple units operating on the Helsinki Metro system in Finland.
  • D. TMB 3000/4000 family of trains
    The TMB 3000/4000 family of trains is a series of electric multiple units used on the Barcelona Metro, known for modernizing and expanding the network’s rolling stock.
  • E. MP-68 trains
    MP-68 trains are a class of rubber-tyred metro rolling stock that have operated on Mexico City’s Metro system since the late 1960s.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e482f788190b78d1588fc26d606 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1577a9c348190b8662142afa832be completed April 29, 2026, 12:57 a.m.
Created at: April 16, 2026, 8:43 p.m.