Triple

T857893
Position Surface form Disambiguated ID Type / Status
Subject Christian Wolff E18533 entity
Predicate deathPlace P21 FINISHED
Object Halle E94413 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: Halle | Statement: [Christian Wolff, deathPlace, Halle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Halle
Context triple: [Christian Wolff, deathPlace, Halle]
  • A. Halle (Saale) chosen
    Halle (Saale) is a major city in the German state of Saxony-Anhalt, known as an important economic, cultural, and educational center, including being home to the Martin Luther University of Halle-Wittenberg.
  • B. Hanover
    Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
  • C. Hilden
    Hilden is a town in western Germany’s North Rhine-Westphalia region, known for its proximity to Düsseldorf and its mix of residential, commercial, and light industrial areas.
  • D. Harrold
    Harrold is a given name and surname, used as a variant spelling of Harold.
  • E. Heilbron
    Heilbron is a town in South Africa’s Free State province that historically served as a parliamentary constituency, including for figures such as Hendrik Verwoerd.
  • 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_69a4938bdd3c8190a954a3c11844d9cf completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac4f740881909cb59a6c18a77af3 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7c018d28881909a3f032e1435970a completed March 4, 2026, 5:16 a.m.
Created at: March 1, 2026, 7:39 p.m.