Triple

T12132309
Position Surface form Disambiguated ID Type / Status
Subject Arts-Loi – Kunst-Wet E288962 entity
Predicate metroLine P848 FINISHED
Object Line 2 E491621 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 2 | Statement: [Arts-Loi – Kunst-Wet, metroLine, Line 2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 2
Context triple: [Arts-Loi – Kunst-Wet, metroLine, Line 2]
  • A. Line 2
    Line 2 is one of the main lines of the Santiago Metro in Chile, running in a generally north–south direction and serving several central and densely populated areas of the city.
  • B. Line 2
    Line 2 is a trolleybus route within Geneva’s public transport system that serves as one of the city’s main electric bus lines.
  • C. Line 2
    Line 2 is a rapid transit line of the Barcelona Metro system that serves several central and northern neighborhoods of the city.
  • D. Line 2
    Line 2 is a major circular line of the Seoul Metropolitan Subway system, known for being one of the busiest and most important routes in the network.
  • E. Line 2 chosen
    Line 2 is a major rapid transit route of the STC Metro system, serving key districts along one of the network’s primary corridors.
  • 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_69d6ab4b5e4c81909950b17151eb0951 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9158b34648190b5eecdf9b07fbb3f completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f68c8ee081908a0331805f62cb0d completed May 2, 2026, 1:05 p.m.
Created at: April 8, 2026, 9:49 p.m.