Triple

T10323703
Position Surface form Disambiguated ID Type / Status
Subject Blackeberg E242704 entity
Predicate metroLine P848 FINISHED
Object Green line E378322 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: Green line | Statement: [Blackeberg, metroLine, Green line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Green line
Context triple: [Blackeberg, metroLine, Green line]
  • A. Green line chosen
    The Green line is one of the main color-coded routes in the Stockholm metro system, serving numerous central and suburban stations across the city.
  • B. Blue line
    The Blue line is one of the main lines of the Stockholm metro system, connecting central Stockholm with several northern and western suburbs.
  • C. Red line
    The Red line is one of the main color-coded routes of the Stockholm metro system, serving numerous central and suburban stations across the city.
  • D. Yellow Line
    The Yellow Line is one of the color-coded rapid transit routes in the Washington Metro system, running primarily in a north–south direction and serving key areas in Washington, D.C. and Northern Virginia.
  • E. Yellow Line
    Yellow Line is one of the principal urban cable car routes in La Paz–El Alto’s Mi Teleférico system, providing high-altitude public transit and panoramic city views.
  • 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d6ce683c8190bf5385dd04bf2de8 completed April 7, 2026, 10:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71da2053481908fe5ed097b480cdd completed April 9, 2026, 3:31 a.m.
Created at: April 6, 2026, 11:50 a.m.