Triple

T11484028
Position Surface form Disambiguated ID Type / Status
Subject Karl-Marx-Platz (Chemnitz) E272227 entity
Predicate hasNearbyCommercialUse P19783 FINISHED
Object shops and restaurants in surrounding buildings LITERAL 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: shops and restaurants in surrounding buildings | Statement: [Karl-Marx-Platz (Chemnitz), hasNearbyCommercialUse, shops and restaurants in surrounding buildings]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasNearbyCommercialUse
Context triple: [Karl-Marx-Platz (Chemnitz), hasNearbyCommercialUse, shops and restaurants in surrounding buildings]
  • A. hasNearbyLandUse chosen
    Indicates that one land area is located close to another area characterized by a specific type of land use.
  • B. nearbyUse
    Indicates that one entity uses or operates another entity that is located nearby or in close physical proximity.
  • C. hasMajorCompanyNearby
    Indicates that a location or entity is situated close to at least one large or significant company.
  • D. connectsToCommercialArea
    Indicates that one location has a direct link, route, or access path to a commercial area.
  • E. hasRecreationalUseNearby
    Indicates that there is at least one location or facility for recreational activities situated close to the referenced entity.
  • F. None of above.

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_69d6aae1b09881909ce2ded3fa0c14fa completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d85a1ea00c8190b42cdc13a6bc61c3 completed April 10, 2026, 2:02 a.m.
PD Predicate disambiguation batch_69d8086ecd6c81908f424864857762d6 completed April 9, 2026, 8:13 p.m.
Created at: April 8, 2026, 9:36 p.m.