Triple

T1559588
Position Surface form Disambiguated ID Type / Status
Subject Eduard Meyer E33287 entity
Predicate workLocation P7 FINISHED
Object Halle (Saale) 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 (Saale) | Statement: [Eduard Meyer, workLocation, Halle (Saale)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Halle (Saale)
Context triple: [Eduard Meyer, workLocation, Halle (Saale)]
  • 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. Halle
    Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
  • C. Dessau
    Dessau is a German city best known for its association with the Bauhaus movement and its iconic modernist architecture.
  • D. 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.
  • E. Wittenau
    Wittenau is a locality in the Reinickendorf borough of Berlin, Germany, known primarily as a residential area with good transport connections.
  • 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_69a885ef9cf48190b0af0f5ce3d02231 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa621102cc81909c5b777a105fc91c completed March 6, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad608813548190b156fb9470ed3239 completed March 8, 2026, 11:42 a.m.
Created at: March 4, 2026, 7:27 p.m.