Triple

T4485346
Position Surface form Disambiguated ID Type / Status
Subject Franconian Alb E107221 entity
Predicate near P350 FINISHED
Object Ingolstadt E130045 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: Ingolstadt | Statement: [Franconian Alb, near, Ingolstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ingolstadt
Context triple: [Franconian Alb, near, Ingolstadt]
  • A. Ingolstadt chosen
    Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
  • B. Stuttgart
    Stuttgart is a major city in southwestern Germany known as an important industrial, cultural, and economic center, particularly famous for its automotive industry and surrounding wine-growing region.
  • C. 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.
  • D. Gauting
    Gauting is a municipality in the district of Starnberg in Bavaria, Germany, known for its residential character and proximity to Munich.
  • E. Schweinfurt
    Schweinfurt is a city in northern Bavaria, Germany, historically known for its ball bearing industry and as a strategic target during World War II.
  • 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_69bd52a758f48190b6b59ca0d9207c2a completed March 20, 2026, 1:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1351018b48190910c3dad6c0f9850 completed March 23, 2026, 12:41 p.m.
Created at: March 20, 2026, 12:59 p.m.