Triple

T765996
Position Surface form Disambiguated ID Type / Status
Subject Schloss Tegel E16175 entity
Predicate locatedNear P294 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: [Schloss Tegel, locatedNear, Lake Tegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Tegel
Context triple: [Schloss Tegel, locatedNear, 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. Großer Wannsee lake
    Großer Wannsee lake is a popular recreational lake in southwestern Berlin, known for its beaches, sailing, and proximity to historically significant sites.
  • C. Schlachtensee
    Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
  • D. Simly Lake
    Simly Lake is a major freshwater reservoir and popular recreational spot located in the Margalla Hills near Islamabad, Pakistan.
  • E. Kankaria Lake
    Kankaria Lake is a historic, man-made lake in Ahmedabad, India, known for its recreational facilities, zoo, and popular waterfront promenade.
  • 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_69a493684ee48190bd43b7c78da4aec8 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a69fb6ac8190bda41852ea01842c completed March 1, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69a792851cc481908d019836ff98d46d completed March 4, 2026, 2:01 a.m.
Created at: March 1, 2026, 7:37 p.m.