Triple

T18437374
Position Surface form Disambiguated ID Type / Status
Subject Kurashiki E450427 entity
Predicate hasSisterCity P919 FINISHED
Object Kaiserslautern 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: Kaiserslautern | Statement: [Kurashiki, hasSisterCity, Kaiserslautern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaiserslautern
Context triple: [Kurashiki, hasSisterCity, Kaiserslautern]
  • A. Kaiserslautern chosen
    Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
  • B. Heppenheim
    Heppenheim is a historic town in southwestern Germany, known for its picturesque old town, vineyards, and location on the Bergstraße at the edge of the Odenwald.
  • C. Karlsruhe
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • D. Saarbrücken
    Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
  • E. Mannheim
    Mannheim is a major city in southwestern Germany, known as an important industrial, commercial, and cultural center at the confluence of the Rhine and Neckar rivers.
  • 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_69d8d381d6388190a9e94e9c658174e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e51c0e04508190bc851a8954ae60e8 completed April 19, 2026, 6:16 p.m.
Created at: April 10, 2026, 11:29 a.m.