Triple

T829239
Position Surface form Disambiguated ID Type / Status
Subject Einstein family E17925 entity
Predicate associatedWithPlace P2830 FINISHED
Object Ulm E9969 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: Ulm | Statement: [Einstein family, associatedWithPlace, Ulm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ulm
Context triple: [Einstein family, associatedWithPlace, Ulm]
  • A. Ulm chosen
    Ulm is a historic city in the German state of Baden-Württemberg, best known for its towering Gothic cathedral and as the birthplace of physicist Albert Einstein.
  • B. Augsburg
    Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
  • C. Neu-Ulm
    Neu-Ulm is a Bavarian town in southern Germany located across the Danube River from the city of Ulm, forming a closely linked urban area with it.
  • D. Günzburg
    Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
  • E. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • 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_69a4937c9c188190aaa216f6b466f452 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ab9b458881909aa23f0eb7cbc87f completed March 1, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69af2b3a21248190aca7710ae6ad6478 completed March 9, 2026, 8:19 p.m.
Created at: March 1, 2026, 7:38 p.m.