Triple

T9540416
Position Surface form Disambiguated ID Type / Status
Subject Regen E230141 entity
Predicate locatedNear P294 FINISHED
Object Deggendorf E376928 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: Deggendorf | Statement: [Regen, locatedNear, Deggendorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Deggendorf
Context triple: [Regen, locatedNear, Deggendorf]
  • A. Deggendorf chosen
    Deggendorf is a town in southeastern Germany situated on the Danube River, known as a regional commercial and transportation hub near the Bavarian Forest.
  • B. Forchheim
    Forchheim is a town in Upper Franconia, Bavaria, Germany, known for its historic old town and location along major regional rail and road routes.
  • C. Kronach
    Kronach is a historic town in northern Bavaria, Germany, known for its well-preserved medieval old town and the imposing Rosenberg Fortress.
  • D. Werdau
    Werdau is a town in the Free State of Saxony in eastern Germany, historically known for its textile and engineering industries.
  • E. Amberg
    Amberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former role as a regional administrative and trading center.
  • 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_69ca847b1b3081908f72bc932c17cc41 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98e695948190ab107fff38c57de7 completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6f69ad0448190a2f472555384f0be completed April 9, 2026, 12:45 a.m.
Created at: March 30, 2026, 8:01 p.m.