Triple

T14109291
Position Surface form Disambiguated ID Type / Status
Subject Department of Digitalization E339591 entity
Predicate locatedIn P40 FINISHED
Object Frederiksberg E255466 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: Frederiksberg | Statement: [Department of Digitalization, locatedIn, Frederiksberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frederiksberg
Context triple: [Department of Digitalization, locatedIn, Frederiksberg]
  • A. Frederiksberg chosen
    Frederiksberg is an affluent, centrally located municipality in Denmark that forms an enclave within the city of Copenhagen and is known for its parks, cultural institutions, and historic architecture.
  • B. Valby
    Valby is a district in Copenhagen, Denmark, known as an important local transport and residential area within the city.
  • C. Hellerup
    Hellerup is a suburban district just north of central Copenhagen, known for its affluent residential areas, seaside location, and role as a key transport and commercial hub.
  • D. Herlev
    Herlev is a suburban municipality and town in the Capital Region of Denmark, located just northwest of central Copenhagen.
  • E. Gentofte
    Gentofte is a suburban municipality just north of central Copenhagen in eastern Denmark, known for its affluent residential areas and proximity to the Øresund coast.
  • 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_69d81c69b5c8819094aa1abf18302908 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de600caf308190ab6d8451ed4e3797 completed April 14, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe8bbd051c8190a89f9801a7b08b2d completed May 9, 2026, 1:19 a.m.
Created at: April 9, 2026, 10:22 p.m.