Triple

T12191396
Position Surface form Disambiguated ID Type / Status
Subject Fallersleben E290470 entity
Predicate locatedIn P40 FINISHED
Object Niedersachsen 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: Niedersachsen | Statement: [Fallersleben, locatedIn, Niedersachsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Niedersachsen
Context triple: [Fallersleben, locatedIn, Niedersachsen]
  • 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. Hamburg state
    Hamburg state is a federal state of Germany that consists primarily of the city of Hamburg, a major northern European port and cultural center.
  • C. 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.
  • D. Schleswig-Holstein
    Schleswig-Holstein is Germany’s northernmost state, known for its North Sea and Baltic Sea coastlines, maritime heritage, and shared border with Denmark.
  • E. Mecklenburg-Vorpommern
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • 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_69d6ab64de5881908d56eb7a75c6cc69 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91c5340248190b79379423f3a3ca1 completed April 10, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64b85782481908cca14d8e8345411 completed May 2, 2026, 7:07 p.m.
Created at: April 8, 2026, 9:50 p.m.