Triple

T4489501
Position Surface form Disambiguated ID Type / Status
Subject Ulm University E107333 entity
Predicate city P40 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: [Ulm University, city, Ulm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ulm
Context triple: [Ulm University, city, 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. Heilbronn
    Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
  • C. 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.
  • D. 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.
  • E. Günzburg
    Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
  • 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_69bd43f84f788190a1383579c4a595be completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd556d29f08190bab1e872dd7e819f completed March 20, 2026, 2:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16e581e94819086313b9d3a40b159 completed March 23, 2026, 4:46 p.m.
Created at: March 20, 2026, 12:59 p.m.