Triple

T20094139
Position Surface form Disambiguated ID Type / Status
Subject Karlskoga E496352 entity
Predicate locatedNear P294 FINISHED
Object Lake Möckeln E455452 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 Möckeln | Statement: [Karlskoga, locatedNear, Lake Möckeln]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Möckeln
Context triple: [Karlskoga, locatedNear, Lake Möckeln]
  • A. Lake Möckeln chosen
    Lake Möckeln is a freshwater lake in central Sweden known for bordering the industrial town of Karlskoga and offering recreational activities such as fishing and boating.
  • B. Mirower See
    Mirower See is a lake in the Mecklenburg Lake District of northeastern Germany, known for its scenic surroundings and recreational water activities.
  • C. Kulkwitzer See
    Kulkwitzer See is a popular lake and leisure area in Saxony, Germany, known for swimming, diving, and other outdoor recreational activities.
  • D. Wandlitzsee
    Wandlitzsee is a scenic lake in Brandenburg, Germany, known for recreation, bathing, and its proximity to the village of Wandlitz.
  • E. Schlachtensee
    Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
  • 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_69da626eee3881909f3454986d4a6511 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6666a7a94819080ebabfba9762f97 completed April 20, 2026, 5:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a082709bec8819085418d3d93108d81 completed May 16, 2026, 8:12 a.m.
Created at: April 11, 2026, 11:23 p.m.