Triple

T2919697
Position Surface form Disambiguated ID Type / Status
Subject Kungsholmen E78688 entity
Predicate hasMetroStation P522 FINISHED
Object Stadshagen E527298 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: Stadshagen | Statement: [Kungsholmen, hasMetroStation, Stadshagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stadshagen
Context triple: [Kungsholmen, hasMetroStation, Stadshagen]
  • A. Stadshagen chosen
    Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • B. Sassenheim
    Sassenheim is a town in the Dutch province of South Holland, known historically for its bulb-growing industry and location within the Duin- en Bollenstreek (Dune and Bulb Region).
  • C. Heemstede
    Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
  • D. Werl
    Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
  • E. Veldhoven
    Veldhoven is a town and municipality in the southern Netherlands, located near Eindhoven in the province of North Brabant.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a53f8c8190b188d549f1161e84 completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfe6131cd481909bbd9eb896258c68 completed March 22, 2026, 12:52 p.m.
Created at: March 8, 2026, 2:54 p.m.