Triple

T20481772
Position Surface form Disambiguated ID Type / Status
Subject Merseburg district E502472 entity
Predicate hasCentralTown P1474 FINISHED
Object Merseburg NE NERFINISHED

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: Merseburg | Statement: [Merseburg district, hasCentralTown, Merseburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Merseburg
Context triple: [Merseburg district, hasCentralTown, Merseburg]
  • A. Merseburg chosen
    Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
  • B. Halberstadt
    Halberstadt is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and role as a former episcopal seat.
  • C. Querfurt
    Querfurt is a small historic town in the German state of Saxony-Anhalt, known for its well-preserved medieval castle and old town.
  • D. Hatheburg of Merseburg
    Hatheburg of Merseburg was a Saxon noblewoman best known as the first wife of Henry the Fowler, later King Henry I of Germany.
  • E. Nordhausen
    Nordhausen is a historic town in central Germany known for its medieval architecture, former role as a key trading center, and association with the nearby Mittelbau-Dora concentration camp site.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4af32848190aea80682b44d5d6e completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e69b57fa9c819091d12320d46a0cee completed April 20, 2026, 9:32 p.m.
Created at: April 16, 2026, 11:34 a.m.