Triple

T3131596
Position Surface form Disambiguated ID Type / Status
Subject Daniel Burnham E65426 entity
Predicate placeOfDeath P21 FINISHED
Object Heidelberg, Germany E15415 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: Heidelberg, Germany | Statement: [Daniel Burnham, placeOfDeath, Heidelberg, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heidelberg, Germany
Context triple: [Daniel Burnham, placeOfDeath, Heidelberg, Germany]
  • A. Tübingen, Germany
    Tübingen, Germany, is a historic university town in the state of Baden-Württemberg known for its medieval old town and renowned Eberhard Karls University.
  • B. Heidelberg chosen
    Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
  • C. Giessen, Germany
    Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
  • D. Weinheim, Germany
    Weinheim, Germany is a town in the state of Baden-Württemberg known for its historic old town, twin castles, and role as a regional economic and publishing center.
  • E. Würzburg, Germany
    Würzburg, Germany is a historic city in northern Bavaria known for its baroque and rococo architecture, prominent university, and renowned Franconian wine culture.
  • 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_69ad8581c25c8190b0d85ba9b9baa531 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada54b0e688190b691771f17f4c721 completed March 8, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69b224d860dc819095a864619bc29411 completed March 12, 2026, 2:28 a.m.
Created at: March 8, 2026, 3:04 p.m.