Triple

T22829190
Position Surface form Disambiguated ID Type / Status
Subject Karlsruhe district E565749 entity
Predicate borders P224 FINISHED
Object Speyer NE NERFINISHED

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: Speyer | Statement: [Karlsruhe district, borders, Speyer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Speyer
Context triple: [Karlsruhe district, borders, Speyer]
  • A. Speyer chosen
    Speyer is a historic city in southwestern Germany on the Rhine River, renowned for its Romanesque imperial cathedral, a UNESCO World Heritage Site.
  • B. Trier
    Trier is a historic city in western Germany, renowned as one of the country’s oldest cities with extensive Roman ruins and medieval landmarks.
  • C. Mainz
    Mainz is a historic German city on the Rhine River known as a major ecclesiastical and political center of the Holy Roman Empire and today as the capital of the state of Rhineland-Palatinate.
  • D. Lingolsheim
    Lingolsheim is a suburban commune in northeastern France, located just southwest of Strasbourg in the Grand Est region.
  • E. Saarbrücken
    Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e24585ab1c81909b2b5065d15805d5 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17e2a0e308190941064965346f890 completed April 29, 2026, 3:42 a.m.
Created at: April 17, 2026, 3:34 p.m.