Triple

T437323
Position Surface form Disambiguated ID Type / Status
Subject Svante Pääbo E10037 entity
Predicate workLocation P7 FINISHED
Object Leipzig E38199 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: Leipzig | Statement: [Svante Pääbo, workLocation, Leipzig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leipzig
Context triple: [Svante Pääbo, workLocation, Leipzig]
  • A. Leipzig chosen
    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.
  • B. Dresden
    Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
  • C. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • D. Schwerin
    Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
  • E. Frankfurt (Oder)
    Frankfurt (Oder) is a German city on the Oder River at the Polish border, known as a historic university and trade center in the state of Brandenburg.
  • 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_69a2e8465ef481909655c681b01e2986 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2ef26bb78819089b3b5dac0330619 completed Feb. 28, 2026, 1:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbefde5e48190881014a725e9411f completed March 8, 2026, 12:12 a.m.
Created at: Feb. 28, 2026, 1:11 p.m.