Triple

T8031667
Position Surface form Disambiguated ID Type / Status
Subject Leuna E186997 entity
Predicate locatedNear P294 FINISHED
Object Merseburg E213906 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: Merseburg | Statement: [Leuna, locatedNear, Merseburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Merseburg
Context triple: [Leuna, locatedNear, 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. 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.
  • D. Eutin
    Eutin is a historic town in northern Germany’s Schleswig-Holstein region, known for its picturesque lakeside setting, castle, and cultural festivals.
  • E. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • 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_69ca82ae2d1081909dbfee42b41db419 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3eef921081908d0ea21f142c175a completed March 31, 2026, 3:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde6dc7b248190b59187a80b4fe036 completed April 2, 2026, 3:47 a.m.
Created at: March 30, 2026, 5:22 p.m.