Triple

T715510
Position Surface form Disambiguated ID Type / Status
Subject Hesse E14304 entity
Predicate containsCity P294 FINISHED
Object Kassel E210960 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: Kassel | Statement: [Hesse, containsCity, Kassel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kassel
Context triple: [Hesse, containsCity, Kassel]
  • A. Kassel chosen
    Kassel is a city in central Germany known for its cultural institutions and as the host of the renowned contemporary art exhibition documenta.
  • B. 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.
  • C. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • D. Gotha
    Gotha is a historic German city in Thuringia known for its former ducal court, cultural heritage, and role as a residence of various German noble houses.
  • E. Darmstadt
    Darmstadt is a city in the German state of Hesse known for its historical ties to the Grand Duchy of Hesse and its role as a center of science, technology, and Art Nouveau culture.
  • 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_69a4934a36e081909e7abef98b898a4e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a574b4d881908b6d0be386081efd completed March 1, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69aea8034cf0819089240d70f0a346a6 completed March 9, 2026, 10:59 a.m.
Created at: March 1, 2026, 7:37 p.m.