Triple

T3231342
Position Surface form Disambiguated ID Type / Status
Subject Willem van Nassau E67746 entity
Predicate birthPlace P1 FINISHED
Object Dillenburg E170913 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: Dillenburg | Statement: [Willem van Nassau, birthPlace, Dillenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dillenburg
Context triple: [Willem van Nassau, birthPlace, Dillenburg]
  • A. Dillenburg chosen
    Dillenburg is a historic town in the German state of Hesse, known as the ancestral seat of the House of Orange-Nassau and its connection to Dutch history.
  • B. Neustadt
    Neustadt is a district of the Austrian city of Salzburg, known for its central urban character within the historic and cultural landscape of the city.
  • C. Neustadt
    Neustadt is a vibrant district of Dresden, Germany, known for its historic architecture, lively arts scene, and numerous bars, cafes, and cultural venues.
  • D. Schlettstadt
    Schlettstadt, now known as Sélestat, is a historic town in the Alsace region of northeastern France noted for its medieval architecture and humanist heritage.
  • E. Marburg
    Marburg is a historic university town in central Germany known for its well-preserved medieval old town and the Philipps-Universität, one of the oldest Protestant universities in the world.
  • 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_69ad858c61888190a31196310d9b30b5 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaed99d2c8190950fa883ec6f1f8e completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b28e9f56b881908742f2aff68b2a34 completed March 12, 2026, 9:59 a.m.
Created at: March 8, 2026, 3:08 p.m.