Triple

T16273842
Position Surface form Disambiguated ID Type / Status
Subject Mölndal E395071 entity
Predicate hasTwinTown P919 FINISHED
Object Naestved E272601 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: Naestved | Statement: [Mölndal, hasTwinTown, Naestved]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Naestved
Context triple: [Mölndal, hasTwinTown, Naestved]
  • A. Næstved chosen
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • B. Nakskov
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • C. Vordingborg
    Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
  • D. Thisted
    Thisted is a coastal town and municipality in northwestern Jutland, Denmark, known for its scenic location by the Limfjord and its role as a regional commercial and cultural center.
  • E. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • 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_69d87f221d8081909b0b2063e7528ba2 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2460b22d88190bdc7cf509cf74198 completed April 17, 2026, 2:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00354ca28081908f993619a332cbf6 completed May 10, 2026, 7:35 a.m.
Created at: April 10, 2026, 5:05 a.m.