Triple

T9859070
Position Surface form Disambiguated ID Type / Status
Subject Franconian Forest E239659 entity
Predicate nearbyCity P350 FINISHED
Object Kronach E375518 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: Kronach | Statement: [Franconian Forest, nearbyCity, Kronach]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kronach
Context triple: [Franconian Forest, nearbyCity, Kronach]
  • A. Kronach chosen
    Kronach is a historic town in northern Bavaria, Germany, known for its well-preserved medieval old town and the imposing Rosenberg Fortress.
  • B. Kulmbach
    Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
  • C. Schleiz
    Schleiz is a historic town in eastern Thuringia, Germany, known for its role as a former princely residence and for the nearby Schleizer Dreieck motor racing circuit.
  • D. Markranstädt
    Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
  • E. Döbeln
    Döbeln is a small town in the German state of Saxony, known for its historic center and location between the cities of Leipzig, Dresden, and Chemnitz.
  • 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_69ca84e6493081909cf58c8d42ea856b completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb39b06b48190ab53ff00ff0513ca completed April 2, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69e623a9711081908eac4238a717305c completed April 20, 2026, 1:01 p.m.
Created at: March 30, 2026, 8:35 p.m.