Triple

T4369799
Position Surface form Disambiguated ID Type / Status
Subject Pegnitz E98867 entity
Predicate flowsThrough P225 FINISHED
Object Nürnberg E408998 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: Nürnberg | Statement: [Pegnitz, flowsThrough, Nürnberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nürnberg
Context triple: [Pegnitz, flowsThrough, Nürnberg]
  • A. Munich
    Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
  • B. Stadt Nürnberg chosen
    Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
  • C. Ingolstadt
    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.
  • D. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • E. Würzburg
    Würzburg is a historic city in southern Germany known for its baroque architecture, the Würzburg Residence palace, and its location along the Main River in the Franconia wine region.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b352052b388190b02cca9a3b480be4 completed March 12, 2026, 11:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf6c27ce8c81908253c7639207fd3c completed March 22, 2026, 4:12 a.m.
Created at: March 12, 2026, 11:17 p.m.