Triple

T19632789
Position Surface form Disambiguated ID Type / Status
Subject Gmunden E471312 entity
Predicate hasLandmark P105 FINISHED
Object Seeschloss Ort NE NERFINISHED

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: Seeschloss Ort | Statement: [Gmunden, hasLandmark, Seeschloss Ort]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seeschloss Ort
Context triple: [Gmunden, hasLandmark, Seeschloss Ort]
  • A. Seeschloss Ort chosen
    Seeschloss Ort is a historic lakeside castle situated on an island in the Traunsee, renowned as a picturesque landmark of the Austrian town of Gmunden.
  • B. Schloss Ort
    Schloss Ort is a historic lakeside castle in Gmunden, Austria, renowned for its picturesque island setting on Lake Traunsee.
  • C. Wendenschloß
    Wendenschloß is a waterside locality in the Berlin borough of Treptow-Köpenick, known for its residential character and access to the Langer See.
  • D. Heiligenhafen
    Heiligenhafen is a coastal town in northern Germany on the Baltic Sea, known for its fishing harbor, beaches, and tourism.
  • E. Jever Castle
    Jever Castle is a historic moated castle in the town of Jever in Lower Saxony, Germany, known for its Renaissance architecture and museum.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e511f28481909f4bc3ea9191e54a completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e6410449ec8190b8c20c0e09cd9156 completed April 20, 2026, 3:06 p.m.
Created at: April 10, 2026, 1:44 p.m.