Triple

T19248661
Position Surface form Disambiguated ID Type / Status
Subject E4 E481328 entity
Predicate passesThrough P225 FINISHED
Object Helsingborg NE NERFINISHED

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: Helsingborg | Statement: [E4, passesThrough, Helsingborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helsingborg
Context triple: [E4, passesThrough, Helsingborg]
  • A. Halmstad
    Halmstad is a coastal city in southwestern Sweden known for its historic town center, harbor, and role as a strategic site in Scandinavian conflicts.
  • B. Halmstad
    Halmstad is a village in Moss municipality in Viken county, southeastern Norway.
  • C. Helsingborg, Sweden chosen
    Helsingborg, Sweden is a coastal city in southern Sweden known for its historic architecture, strategic location on the Öresund Strait, and role as a regional commercial and cultural center.
  • D. Helsinge
    Helsinge is a town in North Zealand, Denmark, known as a local commercial and transport hub connected by rail to nearby cities including Hillerød.
  • E. Malmö
    Malmö is a major coastal city in southern Sweden known for its historic center, modern architecture like the Turning Torso, and its role as a cultural and economic hub connected to Copenhagen via the Öresund Bridge.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8cd9d1081908a181d02b88b59b8 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fb2f43e48190abab5257bec8e6aa completed April 20, 2026, 10:08 a.m.
Created at: April 10, 2026, 1:27 p.m.