Triple

T633342
Position Surface form Disambiguated ID Type / Status
Subject German Aerospace Center E15966 entity
Predicate hasResearchCenterIn P11730 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: [German Aerospace Center, hasResearchCenterIn, Dresden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dresden
Context triple: [German Aerospace Center, hasResearchCenterIn, 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. Cottbus
    Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
  • E. Neustrelitz
    Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
  • 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a515ceb081908c064b2082047c0f completed March 1, 2026, 8:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69adc96f0704819088ec7b4b8f37736e completed March 8, 2026, 7:09 p.m.
Created at: March 1, 2026, 7:35 p.m.