Triple

T2321206
Position Surface form Disambiguated ID Type / Status
Subject Herning E51183 entity
Predicate locatedIn P40 FINISHED
Object Herning Municipality E51183 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: Herning Municipality | Statement: [Herning, locatedIn, Herning Municipality]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herning Municipality
Context triple: [Herning, locatedIn, Herning Municipality]
  • A. Herning chosen
    Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
  • B. Hillerød
    Hillerød is a Danish town on the island of Zealand, known for the historic Frederiksborg Castle and its role as a regional administrative and cultural center.
  • C. Vejle
    Vejle is a Danish city known for its scenic fjord setting, rolling hills, and role as a regional commercial and transportation hub in southeastern Jutland.
  • D. Middelfart
    Middelfart is a coastal town in central Denmark known for its location on the island of Funen by the Little Belt strait and its historic role as a trading and ferry port.
  • E. Hjørring
    Hjørring is a historic town in northern Denmark known as one of the oldest settlements in the Vendsyssel region and a local commercial and cultural center.
  • 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_69a88b074b908190ae983dbca7757d88 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc6337e948190bb4860f7045914e1 completed March 7, 2026, 6:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae896911908190b53954dbf854cc18 completed March 9, 2026, 8:48 a.m.
Created at: March 4, 2026, 7:49 p.m.