Triple

T8708515
Position Surface form Disambiguated ID Type / Status
Subject Van Ness E206713 entity
Predicate metroLine P848 FINISHED
Object Red Line E26147 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: Red Line | Statement: [Van Ness, metroLine, Red Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Red Line
Context triple: [Van Ness, metroLine, Red Line]
  • A. Red Line
    The Red Line is a major rapid transit route in Chicago that runs north–south through the city, serving as one of the busiest lines in its subway and elevated rail system.
  • B. Red Line chosen
    Red Line is a major rapid transit route in the Washington Metro system, running through key areas of Washington, D.C., and its Maryland suburbs.
  • C. Red Line
    Red Line is one of the main rapid transit routes of the Dubai Metro, running along key areas of the city and serving many of its major commercial and residential districts.
  • D. Red Line
    The Red Line is one of the main lines of the Lisbon Metro system, connecting key transport hubs and eastern districts of Portugal’s capital.
  • E. Red Line
    The Red Line is one of the major corridors of the Delhi Metro rapid transit system, serving numerous densely populated areas in and around Delhi.
  • 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_69ca835645e881908f00e3c8b51da81d completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc58ffa6a481908866b6239d1d9b92 completed March 31, 2026, 11:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf28b78e90819098ab1d4877ab88fe completed April 3, 2026, 2:40 a.m.
Created at: March 30, 2026, 6:35 p.m.