Triple

T2926506
Position Surface form Disambiguated ID Type / Status
Subject Lake Tegel E78856 entity
Predicate adjacentTo P224 FINISHED
Object Heiligensee E78332 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: Heiligensee | Statement: [Lake Tegel, adjacentTo, Heiligensee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heiligensee
Context triple: [Lake Tegel, adjacentTo, Heiligensee]
  • A. Heiligensee chosen
    Heiligensee is a residential and partly lakeside locality in the northwest of Berlin, known for its green spaces and village-like character within the borough of Reinickendorf.
  • B. Schlachtensee
    Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
  • C. Müggelsee
    Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
  • D. Schwielowsee
    Schwielowsee is a scenic lake and municipality in Brandenburg, Germany, known for its natural landscapes, water recreation, and proximity to Potsdam.
  • E. Lake Müritz
    Lake Müritz is Germany’s second-largest lake and a central feature of the Mecklenburg Lake District, known for its extensive wetlands, rich birdlife, and surrounding protected landscapes.
  • 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_69ad8b0d40b481908bc2a5fa2e73c3fb completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad97c1e9c08190bcec80bc3262697a completed March 8, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69b08668a204819082b13e6ce62d5728 completed March 10, 2026, 9 p.m.
Created at: March 8, 2026, 2:55 p.m.