Triple

T17273909
Position Surface form Disambiguated ID Type / Status
Subject Můstek station E419335 entity
Predicate hasTransferTo P17241 FINISHED
Object Line A E390322 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: Line A | Statement: [Můstek station, hasTransferTo, Line A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line A
Context triple: [Můstek station, hasTransferTo, Line A]
  • A. Line A
    Line A is a line of the Mexico City Metro system that serves the eastern part of the metropolitan area, connecting central Mexico City with several suburban municipalities.
  • B. Line A
    Line A is the main north–south rapid transit line of the Medellín Metro system in Colombia, serving as its busiest and most central corridor.
  • C. Line A
    Line A is the primary route of the Bilbao tram system, serving key areas of the city with modern light rail service.
  • D. Line A chosen
    Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
  • E. Line A
    Line A is one of the main routes of the Porto Metro light rail system in Porto, Portugal, connecting key urban and suburban areas.
  • 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_69d886da626481908a14ce7830329a35 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42f4c209c81909c713ed78f2cb19a completed April 19, 2026, 1:26 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01794d605481908b5e430c3142c203 completed May 11, 2026, 6:38 a.m.
Created at: April 10, 2026, 5:40 a.m.