Triple

T14840178
Position Surface form Disambiguated ID Type / Status
Subject Wakenitz E348940 entity
Predicate sourceLake P947 FINISHED
Object Ratzeburger See E1121887 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: Ratzeburger See | Statement: [Wakenitz, sourceLake, Ratzeburger See]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ratzeburger See
Context triple: [Wakenitz, sourceLake, Ratzeburger See]
  • A. Ratzeburger See chosen
    Ratzeburger See is a large glacial lake in northern Germany known for its scenic surroundings and the town of Ratzeburg situated on an island within it.
  • B. Schlachtensee
    Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
  • C. Schweriner See
    Schweriner See is a large lake in northern Germany that surrounds and characterizes the city of Schwerin, known for its scenic shores and historic lakeside castle.
  • D. Ostorfer See
    Ostorfer See is a lake in the German state of Mecklenburg-Vorpommern, forming part of the lake landscape around the city of Schwerin.
  • E. Segeberger See
    Segeberger See is a lake in Schleswig-Holstein, northern Germany, known for its scenic surroundings and proximity to the town of Bad Segeberg.
  • 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_69d822ec69008190a9232caa68836872 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded28e40f08190b309d8ac6404d2fc completed April 14, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe64fe89e88190912cd205feef85d3 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:53 a.m.