Triple

T624282
Position Surface form Disambiguated ID Type / Status
Subject Harz E14581 entity
Predicate contains P35 FINISHED
Object town of Goslar E51097 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: town of Goslar | Statement: [Harz, contains, town of Goslar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: town of Goslar
Context triple: [Harz, contains, town of Goslar]
  • A. Goslar chosen
    Goslar is a historic German town at the foot of the Harz Mountains, renowned for its well-preserved medieval old town and former silver mines, both recognized as UNESCO World Heritage Sites.
  • B. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • C. Hildesheim Cathedral
    Hildesheim Cathedral is a medieval Romanesque church in Hildesheim, Germany, renowned as a UNESCO World Heritage Site for its architecture and exceptional medieval art treasures.
  • D. Eisleben
    Eisleben is a historic town in the German state of Saxony-Anhalt, best known as the birthplace of Protestant Reformer Martin Luther.
  • E. Lichtenfels
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • 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_69a4934b17c881909ace8270e8ddd202 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e43002c81908e0c7dab29b75978 completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5670380948190954bbdf802ed403c completed March 2, 2026, 10:31 a.m.
Created at: March 1, 2026, 7:35 p.m.