Triple

T15183849
Position Surface form Disambiguated ID Type / Status
Subject WOB E362815 entity
Predicate regionServed P82 FINISHED
Object Wolfsburg urban district E74139 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: Wolfsburg urban district | Statement: [WOB, regionServed, Wolfsburg urban district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wolfsburg urban district
Context triple: [WOB, regionServed, Wolfsburg urban district]
  • A. Wolfsburg chosen
    Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
  • B. Wolfsburg region
    The Wolfsburg region is an area in Lower Saxony, Germany, centered around the city of Wolfsburg and known for its industrial significance, particularly as the headquarters of Volkswagen.
  • C. Wallenhorst
    Wallenhorst is a municipality in Lower Saxony, Germany, located near the city of Osnabrück.
  • D. Hamburg-Waltershof
    Hamburg-Waltershof is a major port and industrial district of Hamburg, Germany, known for its container terminals and logistics facilities within the Port of Hamburg.
  • E. Belzig district
    Belzig district was a former administrative district in the German state of Brandenburg that later became part of the larger Potsdam-Mittelmark district.
  • 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_69d85a09a39c81908759f23268e2d408 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e006674c088190ba635a78c30f5637 completed April 15, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fec893f1e08190a192b7b9b80484e8 completed May 9, 2026, 5:39 a.m.
Created at: April 10, 2026, 3:09 a.m.