Triple

T9093219
Position Surface form Disambiguated ID Type / Status
Subject Gifhorn E217943 entity
Predicate hasTwinTown P919 FINISHED
Object Gardelegen E690448 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: Gardelegen | Statement: [Gifhorn, hasTwinTown, Gardelegen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gardelegen
Context triple: [Gifhorn, hasTwinTown, Gardelegen]
  • A. Gardelegen chosen
    Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
  • B. Lippstadt
    Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
  • C. Detmold
    Detmold is a historic town in northwestern Germany that served as the capital and residence city of the former Principality of Lippe.
  • D. Bielefeld
    Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
  • E. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • 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_69ca83d8ab5881909d8fddae363b32b1 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc96b347d4819085b33d0e20834f47 completed April 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2817858008190b150417f9ef59b48 completed April 5, 2026, 3:36 p.m.
Created at: March 30, 2026, 7:14 p.m.