Triple

T790838
Position Surface form Disambiguated ID Type / Status
Subject Duke of Saxony E16908 entity
Predicate hasCapitalHistorically P14584 FINISHED
Object Dresden E37454 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: Dresden | Statement: [Duke of Saxony, hasCapitalHistorically, Dresden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dresden
Context triple: [Duke of Saxony, hasCapitalHistorically, Dresden]
  • A. Dresden chosen
    Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
  • B. Leipzig
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • C. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • D. Magdeburg
    Magdeburg is a historic city in central Germany, known for its medieval cathedral, role as a major trading and industrial center, and location on the Elbe River.
  • E. Cottbus
    Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
  • 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_69a4936cb7448190914f5fe4b8d81607 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aa9e0f0081909d2a89387d6c08e1 completed March 1, 2026, 9:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae65078b908190a75665a2ee025db6 completed March 9, 2026, 6:13 a.m.
Created at: March 1, 2026, 7:38 p.m.