Triple

T5169163
Position Surface form Disambiguated ID Type / Status
Subject Henrik Ibsen E116631 entity
Predicate residence P75 FINISHED
Object Dresden, Germany 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, Germany | Statement: [Henrik Ibsen, residence, Dresden, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dresden, Germany
Context triple: [Henrik Ibsen, residence, Dresden, Germany]
  • 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. Dresden
    Dresden is a small community within the municipality of Chatham-Kent in southwestern Ontario, Canada, known historically for its role in the Underground Railroad and Black settlement.
  • C. 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.
  • D. Torgau, Germany
    Torgau, Germany is a historic town in Saxony on the Elbe River, known for its Renaissance architecture and its role as a key site in the Protestant Reformation.
  • E. Deggendorf, Germany
    Deggendorf, Germany is a Bavarian town on the Danube River known as a regional commercial and industrial center with strong ties to manufacturing and technology companies.
  • 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_69bd445ff97c81909a2615cc56235470 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd794dd9988190922e138f2a9a3c62 completed March 20, 2026, 4:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69bed93f33ac8190b2f60a8e95685bc8 completed March 21, 2026, 5:45 p.m.
Created at: March 20, 2026, 1:45 p.m.