Triple

T20726510
Position Surface form Disambiguated ID Type / Status
Subject Altmühlsee E509447 entity
Predicate nearbySettlement P350 FINISHED
Object Muhr am See 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: Muhr am See | Statement: [Altmühlsee, nearbySettlement, Muhr am See]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muhr am See
Context triple: [Altmühlsee, nearbySettlement, Muhr am See]
  • A. Muhr am See chosen
    Muhr am See is a small Bavarian municipality in Germany known for its location on Lake Altmühlsee and its rich birdlife and nature conservation areas.
  • B. Mörbisch am See
    Mörbisch am See is an Austrian lakeside village on the shore of Lake Neusiedl, renowned for its open-air operetta festival and traditional wine culture.
  • C. Seewiesen
    Seewiesen is a research locality in Bavaria, Germany, best known for its ornithological and behavioral science institutes associated with Konrad Lorenz and other pioneering ethologists.
  • D. Riegsee
    Riegsee is a picturesque lake in Bavaria, Germany, known for its clear waters and scenic Alpine surroundings.
  • E. Schliersee
    Schliersee is a picturesque lake and town in the Bavarian Alps of southern Germany, known for its scenic mountain setting and outdoor recreation.
  • 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_69e0b4c4cc648190b45fda6e2b20af56 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c1e8b020819091d5788b90215ead completed April 21, 2026, 12:16 a.m.
Created at: April 16, 2026, 12:29 p.m.