Triple

T2813008
Position Surface form Disambiguated ID Type / Status
Subject Müritz National Park E54215 entity
Predicate locatedNear P294 FINISHED
Object Lake Müritz E301640 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: Lake Müritz | Statement: [Müritz National Park, locatedNear, Lake Müritz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Müritz
Context triple: [Müritz National Park, locatedNear, Lake Müritz]
  • A. Lake Müritz chosen
    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.
  • B. Schlachtensee
    Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
  • C. Heiligensee
    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.
  • D. Müggelsee
    Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
  • E. Lake Schwerin
    Lake Schwerin is a large glacial lake in northern Germany’s Mecklenburg-Vorpommern region, known for its scenic shores, surrounding forests, and proximity to the city of Schwerin.
  • 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_69ab49de0af08190b3da69683be1e728 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde4a31d081909377044d5ff791b0 completed March 7, 2026, 8:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69afe8aecd5081909b38d229904e5bde completed March 10, 2026, 9:47 a.m.
Created at: March 6, 2026, 9:59 p.m.