Triple

T605521
Position Surface form Disambiguated ID Type / Status
Subject Max Born E11585 entity
Predicate deathPlace P21 FINISHED
Object Lower Saxony E4364 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: Lower Saxony | Statement: [Max Born, deathPlace, Lower Saxony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lower Saxony
Context triple: [Max Born, deathPlace, Lower Saxony]
  • A. Lower Saxony chosen
    Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
  • B. Saxony-Anhalt
    Saxony-Anhalt is a federal state in central Germany known for its rich cultural heritage, including numerous UNESCO World Heritage Sites such as the Bauhaus in Dessau and the historic towns of Quedlinburg and Wittenberg.
  • C. Thuringia
    Thuringia is a federal state in central Germany known for its forested landscapes, historic cities like Weimar and Erfurt, and its rich cultural and intellectual heritage.
  • D. Mecklenburg-Vorpommern
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • E. North Rhine-Westphalia
    North Rhine-Westphalia is Germany’s most populous federal state, known for its major industrial regions, cultural hubs like Cologne and Düsseldorf, and numerous universities and research institutions.
  • 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_69a4932779b881908688590d59c71900 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49dc7d88c81909fe493ac57fd784e completed March 1, 2026, 8:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69acaca1f0988190aa95e12ecc86398e completed March 7, 2026, 10:54 p.m.
Created at: March 1, 2026, 7:35 p.m.