Triple

T10950626
Position Surface form Disambiguated ID Type / Status
Subject Elphi E258715 entity
Predicate region P40 FINISHED
Object Hamburg state E902273 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: Hamburg state | Statement: [Elphi, region, Hamburg state]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hamburg state
Context triple: [Elphi, region, Hamburg state]
  • A. Hamburg state chosen
    Hamburg state is a federal state of Germany that consists primarily of the city of Hamburg, a major northern European port and cultural center.
  • B. Lower Saxony
    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.
  • 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. State of Bremen
    The State of Bremen is a small federal state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port facilities.
  • E. Upper Saxony
    Upper Saxony is a historical region in central Europe that formed part of the Electorate of Saxony and encompassed areas including the city of Dresden.
  • 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_69d6aa88500c819097d7032ca578e74f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770ed2f1c819081ec58457f57889d completed April 9, 2026, 9:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69e3e6fd11b08190b587bebf9586f754 completed April 18, 2026, 8:18 p.m.
Created at: April 8, 2026, 9:23 p.m.