Triple

T5034924
Position Surface form Disambiguated ID Type / Status
Subject Transdanubia E113398 entity
Predicate containsLake P1025 FINISHED
Object Lake Fertő E89503 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 Fertő | Statement: [Transdanubia, containsLake, Lake Fertő]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Fertő
Context triple: [Transdanubia, containsLake, Lake Fertő]
  • A. Lake Balaton
    Lake Balaton is a major Central European freshwater lake in western Hungary, renowned as a popular tourist and recreation destination.
  • B. Lake Neusiedl chosen
    Lake Neusiedl is a large, shallow steppe lake in Central Europe renowned for its unique wetland ecosystem, birdlife, and surrounding wine-growing region.
  • C. Bodrog
    Bodrog is a river in Central Europe that flows through Slovakia and Hungary before joining the Tisza River.
  • D. Ségny
    Ségny is a small commune in eastern France’s Ain department, situated near the Swiss border in the Auvergne-Rhône-Alpes region.
  • E. Rupanco Lake
    Rupanco Lake is a scenic glacial lake in Chile’s Los Lagos Region, known for its clear waters, surrounding volcanoes, and popularity for fishing and outdoor recreation.
  • 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_69bd44384298819089c49e7c330ec7b8 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd73b8646c8190b3cc20193e4639ee completed March 20, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea477efec8190a84a0186f5517a43 completed March 21, 2026, 2 p.m.
Created at: March 20, 2026, 1:36 p.m.