Triple

T19598284
Position Surface form Disambiguated ID Type / Status
Subject Max Rubner Institute E470402 entity
Predicate hasBranch P35 FINISHED
Object Detmold 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: Detmold | Statement: [Max Rubner Institute, hasBranch, Detmold]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Detmold
Context triple: [Max Rubner Institute, hasBranch, Detmold]
  • A. Detmold chosen
    Detmold is a historic town in northwestern Germany that served as the capital and residence city of the former Principality of Lippe.
  • B. Lippstadt
    Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
  • C. Diepholz
    Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
  • D. Meppen
    Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
  • E. Gardelegen
    Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
  • 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_69d8e510024481908415c0d616fa6186 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e6407c52c081908704d3a4dd6e853b completed April 20, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:43 p.m.