Triple

T9703552
Position Surface form Disambiguated ID Type / Status
Subject Somogy County E234838 entity
Predicate borderedBy P224 FINISHED
Object Lake Balaton E31666 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 Balaton | Statement: [Somogy County, borderedBy, Lake Balaton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Balaton
Context triple: [Somogy County, borderedBy, Lake Balaton]
  • A. Lake Balaton chosen
    Lake Balaton is a major Central European freshwater lake in western Hungary, renowned as a popular tourist and recreation destination.
  • B. Lake Neusiedl
    Lake Neusiedl is a large, shallow steppe lake in Central Europe renowned for its unique wetland ecosystem, birdlife, and surrounding wine-growing region.
  • C. Sárospatak
    Sárospatak is a historic town in northeastern Hungary, renowned for its medieval castle and role as a cultural and educational center in the region.
  • D. Biogradsko Lake
    Biogradsko Lake is a glacial lake in northeastern Montenegro renowned for its clear waters and surrounding primeval forest within Biogradska Gora National Park.
  • E. Solina Lake
    Solina Lake is a large artificial reservoir in southeastern Poland, renowned for its scenic mountain setting, hydroelectric dam, and popularity as a tourist and water-sports destination.
  • 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_69ca84cc78808190a56f3402b7c139a7 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d73a0148190ad4178fd462cdd9c completed April 1, 2026, 10:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19f800ec48190bc3028ecb3baeb28 completed April 4, 2026, 11:32 p.m.
Created at: March 30, 2026, 8:18 p.m.