Triple

T7059010
Position Surface form Disambiguated ID Type / Status
Subject Peter Vischer the Elder E164166 entity
Predicate workLocation P7 FINISHED
Object Magdeburg E240455 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: Magdeburg | Statement: [Peter Vischer the Elder, workLocation, Magdeburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magdeburg
Context triple: [Peter Vischer the Elder, workLocation, Magdeburg]
  • A. Magdeburg chosen
    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.
  • 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. Erfurt
    Erfurt is a historic German city in the state of Thuringia, known for its well-preserved medieval old town and as an important cultural and educational center.
  • D. Cottbus
    Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
  • E. Schwerin
    Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
  • 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_69c68861678881909961ddf4d779f750 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e26b2acc8190b212ec77b74c419f completed March 27, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7eebe7d008190990482c7d6b512f5 completed March 28, 2026, 3:07 p.m.
Created at: March 27, 2026, 2:38 p.m.