Triple

T8730430
Position Surface form Disambiguated ID Type / Status
Subject Kaufingerstraße E207240 entity
Predicate hasNearbyGate P84046 FINISHED
Object Karlstor E753497 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: Karlstor | Statement: [Kaufingerstraße, hasNearbyGate, Karlstor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karlstor
Context triple: [Kaufingerstraße, hasNearbyGate, Karlstor]
  • A. Karlstor chosen
    Karlstor is a historic city gate in Munich, Germany, marking the western entrance to the city’s old town and serving as a prominent landmark at the end of the main shopping street.
  • B. Youngstorget
    Youngstorget is a central public square in Oslo, Norway, known as a historic hub for political rallies, labor movement events, and urban social life.
  • C. Hötorget
    Hötorget is a central square in downtown Stockholm known for its market stalls, surrounding shops, and cultural venues.
  • D. Gustav Adolfs torg
    Gustav Adolfs torg is a central public square in Stockholm, Sweden, known for its historic buildings, cultural institutions, and role as a prominent civic and ceremonial space.
  • E. Majorstuen
    Majorstuen is a central neighborhood and transport hub in Oslo, Norway, known for its busy junction of metro and tram lines, shopping streets, and residential 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_69ca8358e4008190898471a59b96c301 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d26d280819085e15d4917c2b9a5 completed March 31, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf42b0f5808190863a1ca3c4e9c8d1 completed April 3, 2026, 4:31 a.m.
Created at: March 30, 2026, 6:37 p.m.