Triple

T14355838
Position Surface form Disambiguated ID Type / Status
Subject Marmorpalais E355968 entity
Predicate locatedOnBodyOfWater P212 FINISHED
Object Heiliger See E355969 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: Heiliger See | Statement: [Marmorpalais, locatedOnBodyOfWater, Heiliger See]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heiliger See
Context triple: [Marmorpalais, locatedOnBodyOfWater, Heiliger See]
  • A. Heiliger See chosen
    Heiliger See is a picturesque lake in Potsdam, Germany, known for its scenic setting amid historic palaces and gardens.
  • B. Weißer See
    Weißer See is a small urban lake and popular recreational spot located in Berlin's Weißensee district.
  • C. Čertovo jezero
    Čertovo jezero is a glacial lake in the Bohemian Forest of the Czech Republic, known for its scenic setting and associated folk legends.
  • D. Lake Velence
    Lake Velence is one of Hungary’s largest natural lakes, known as a popular resort and recreation area in the Transdanubian region.
  • E. Lake Van
    Lake Van is the largest lake in Turkey, a saline endorheic lake renowned for its high altitude, unique ecosystem, and historical Armenian cultural sites along its shores.
  • 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_69d82790a7e08190877e2d349b2e8d8e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8f519bf881908615f4d47e0f77aa completed April 14, 2026, 7:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd4c473fa48190866ab946971e971c completed May 8, 2026, 2:36 a.m.
Created at: April 10, 2026, 1:15 a.m.