Triple

T1839089
Position Surface form Disambiguated ID Type / Status
Subject Main E41131 entity
Predicate hasCityOnBank P7935 FINISHED
Object Bamberg E134924 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: Bamberg | Statement: [Main, hasCityOnBank, Bamberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bamberg
Context triple: [Main, hasCityOnBank, Bamberg]
  • A. Bamberg chosen
    Bamberg is a historic city in northern Bavaria, Germany, renowned for its well-preserved medieval old town and status as a UNESCO World Heritage Site.
  • B. Coburg
    Coburg is a historic town in northern Bavaria, Germany, known for its well-preserved medieval architecture and its former role as the seat of the Duchy of Saxe-Coburg and Gotha.
  • C. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • D. Straubing
    Straubing is a Bavarian town on the Danube River known for its historic city center and role as a regional economic and educational hub.
  • E. Schweinfurt
    Schweinfurt is a city in northern Bavaria, Germany, historically known for its ball bearing industry and as a strategic target during World War II.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb03b3eb08190ae68d8476fc89c7f completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69b54c0e702081909384b804fb1ade90 completed March 14, 2026, 11:52 a.m.
Created at: March 4, 2026, 7:33 p.m.