Triple

T4296393
Position Surface form Disambiguated ID Type / Status
Subject Valentinswerder E99723 entity
Predicate locatedInWaterBody P1714 FINISHED
Object Lake Tegel E78856 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 Tegel | Statement: [Valentinswerder, locatedInWaterBody, Lake Tegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Tegel
Context triple: [Valentinswerder, locatedInWaterBody, Lake Tegel]
  • A. Lake Tegel chosen
    Lake Tegel is a large lake in the northwest of Berlin, Germany, known for its recreational areas, beaches, and surrounding forests.
  • B. Stadtsee
    Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
  • C. Lake Heiligensee
    Lake Heiligensee is a small freshwater lake in the Heiligensee district of Berlin, Germany, known for its recreational use and scenic natural surroundings.
  • D. Lake Seliger
    Lake Seliger is a large glacial lake in central Russia known for its scenic islands, pine forests, and popularity as a nature and recreation destination.
  • E. Glienicker Lake
    Glienicker Lake is a scenic lake on the southwestern edge of Berlin, Germany, known for its historic villas, proximity to the Glienicke Bridge, and location along the Havel waterway.
  • 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3509aebd48190af38f2e37f07869a completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5f5b965c48190990357f4cb4e30cb completed March 14, 2026, 11:56 p.m.
Created at: March 12, 2026, 11:08 p.m.