Triple

T1645058
Position Surface form Disambiguated ID Type / Status
Subject Leine E35561 entity
Predicate region P40 FINISHED
Object Northern Germany E38556 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: Northern Germany | Statement: [Leine, region, Northern Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Northern Germany
Context triple: [Leine, region, Northern Germany]
  • A. Northern Germany chosen
    Northern Germany is the northern region of Germany, bordering the North and Baltic Seas and Denmark, known for its flat landscapes, Hanseatic cities, and cultural and linguistic ties to Scandinavia.
  • B. southern Germany
    Southern Germany is a culturally and economically significant region of Germany known for its Alpine landscapes, historic cities, and strong regional identities such as Bavaria and Baden-Württemberg.
  • C. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • D. Rhineland
    The Rhineland is a historically significant region in western Germany along the Rhine River, long contested as a strategic and economic heartland in European conflicts.
  • E. 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.
  • 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_69a88604618c81908b41f6429c431eb6 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a41e5a08190b97dd1c0b12c662a completed March 5, 2026, 4:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad71a61de8819095005c222ec50810 completed March 8, 2026, 12:55 p.m.
Created at: March 4, 2026, 7:28 p.m.