Triple

T13955817
Position Surface form Disambiguated ID Type / Status
Subject Traunstein E335655 entity
Predicate nearbyLake P17985 FINISHED
Object Chiemsee E41133 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: Chiemsee | Statement: [Traunstein, nearbyLake, Chiemsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chiemsee
Context triple: [Traunstein, nearbyLake, Chiemsee]
  • A. Chiemsee chosen
    Chiemsee is one of Germany’s largest lakes, famed for its scenic Alpine setting and historic islands such as Herrenchiemsee with its royal palace.
  • B. Altmühlsee
    Altmühlsee is an artificial recreational lake in Bavaria, Germany, popular for swimming, sailing, and nature conservation.
  • C. Wörthsee
    Wörthsee is a scenic lake and popular recreational destination in Upper Bavaria, Germany, known for its clear waters and proximity to Munich.
  • D. Tegernsee
    Tegernsee is a picturesque alpine lake in southern Germany renowned for its clear waters, surrounding mountains, and popular spa and resort towns.
  • E. Königssee
    Königssee is a picturesque alpine lake in southeastern Germany, renowned for its emerald-green waters, steep surrounding mountains, and status as one of the cleanest lakes in the country.
  • 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_69d81c61f3508190aaf2ca0dc0002c59 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2e78a4a481908e438745631a43c0 completed April 14, 2026, 12:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdef3f31c8190a2fc3eed316756d6 completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 10:17 p.m.