Triple

T15469383
Position Surface form Disambiguated ID Type / Status
Subject Inner Loop E372116 entity
Predicate servesLine P839 FINISHED
Object Brown Line E140162 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: Brown Line | Statement: [Inner Loop, servesLine, Brown Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Brown Line
Context triple: [Inner Loop, servesLine, Brown Line]
  • A. Brown Line chosen
    The Brown Line is a rapid transit route of Chicago's "L" system that primarily serves the city's North Side and northwest neighborhoods.
  • B. Orange Line
    The Orange Line is one of the primary rapid transit routes in Montreal’s Metro system, running in a U-shaped corridor that connects several major residential and commercial districts across the city.
  • C. Orange Line
    The Orange Line is a rapid transit line in the Kaohsiung Mass Rapid Transit (KMRT) system in Kaohsiung, Taiwan.
  • D. Orange Line
    The Orange Line is one of the primary rapid transit routes in the Washington Metro system, running east–west through Washington, D.C. and its Virginia and Maryland suburbs.
  • E. Orange Line
    The Orange Line is a light rail route in the Dallas Area Rapid Transit (DART) system serving key destinations including Dallas/Fort Worth International Airport and several northern suburbs.
  • 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_69d85cc8bd308190886949510b42e764 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f6b49788190b270fdfe92646842 completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a000ebe275c819094473d37cf33c7d0 completed May 10, 2026, 4:51 a.m.
Created at: April 10, 2026, 3:33 a.m.