Triple

T5021223
Position Surface form Disambiguated ID Type / Status
Subject Kinzig E112852 entity
Predicate flowsThrough P225 FINISHED
Object Offenburg E252092 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: Offenburg | Statement: [Kinzig, flowsThrough, Offenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Offenburg
Context triple: [Kinzig, flowsThrough, Offenburg]
  • A. Offenburg chosen
    Offenburg is a city in southwestern Germany’s Baden-Württemberg state, known as a regional economic and transport hub near the French border in the Upper Rhine region.
  • B. Freudenstadt
    Freudenstadt is a spa and holiday town in southwestern Germany known for its large market square and location in the northern Black Forest.
  • C. Ebingen
    Ebingen is a district of Albstadt in the Swabian Jura region of Baden-Württemberg, Germany, historically known as an independent town and the birthplace of former German Chancellor Kurt Georg Kiesinger.
  • D. Rottweil
    Rottweil is a historic town in southwestern Germany known for its medieval architecture and as the namesake of the Rottweiler dog breed.
  • E. Blaubeuren
    Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
  • 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_69bd4435c2f48190be593158cbfcf8a3 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd736399ac8190aa38efc4b4edc6a2 completed March 20, 2026, 4:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c10791159c81909b91cd5beb962e39 completed March 23, 2026, 9:27 a.m.
Created at: March 20, 2026, 1:36 p.m.