Triple

T7059760
Position Surface form Disambiguated ID Type / Status
Subject Köpenick E164184 entity
Predicate hasGreenSpace P1495 FINISHED
Object Köpenicker Stadtforst E637959 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: Köpenicker Stadtforst | Statement: [Köpenick, hasGreenSpace, Köpenicker Stadtforst]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Köpenicker Stadtforst
Context triple: [Köpenick, hasGreenSpace, Köpenicker Stadtforst]
  • A. Köpenicker Forst chosen
    Köpenicker Forst is a large forested nature reserve in the Köpenick district of Berlin, known for its extensive woodlands, wetlands, and recreational trails.
  • B. Tegeler Forst
    Tegeler Forst is a large forested area in the Berlin district of Tegel, known for its natural landscapes, walking trails, and recreational opportunities.
  • C. Schorfheide forest
    Schorfheide forest is a large historic woodland and former royal hunting reserve in Brandenburg, Germany, known for its rich biodiversity and use as a retreat by political leaders.
  • D. Amager Fælled
    Amager Fælled is a large urban nature reserve on Copenhagen’s Amager Island, known for its wetlands, meadows, and rich biodiversity amid the city.
  • E. Westfalenpark
    Westfalenpark is a large public park in Dortmund, Germany, known for its extensive green spaces, gardens, and the landmark Florian television tower.
  • 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_69c688796c148190adb2f1596f595f22 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e458ad9c81908c3f492b317ce291 completed March 27, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7944809348190bfc96df73f1363b3 completed March 28, 2026, 8:41 a.m.
Created at: March 27, 2026, 2:38 p.m.