Triple

T6842945
Position Surface form Disambiguated ID Type / Status
Subject Wittenau E157817 entity
Predicate hasRoadConnection P385 FINISHED
Object Bundesstraße 96 E318592 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: Bundesstraße 96 | Statement: [Wittenau, hasRoadConnection, Bundesstraße 96]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bundesstraße 96
Context triple: [Wittenau, hasRoadConnection, Bundesstraße 96]
  • A. Bundesstraße 96 chosen
    Bundesstraße 96 is a major German federal highway running in a north–south direction, notably connecting Berlin with the Baltic Sea island of Rügen.
  • B. Bundesstraße 8
    Bundesstraße 8 is a major German federal highway running east–west through several states and connecting numerous towns and cities.
  • C. Bundesstraße 7
    Bundesstraße 7 is a major German federal highway running east–west across several states and connecting numerous cities and regions.
  • D. Bundesstraße 10
    Bundesstraße 10 is a major federal highway in southern Germany that runs east–west and connects several important cities in the states of Baden-Württemberg and Rhineland-Palatinate.
  • E. Bundesstraße 3
    Bundesstraße 3 is a major German federal highway running north–south through several states and connecting numerous cities and towns.
  • 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_69c6882ed4c081909dc465a7cf8838be completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d6b7179481909e3482fef47b2719 completed March 27, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72fb9a8c08190993807f1a4c54184 completed March 28, 2026, 1:32 a.m.
Created at: March 27, 2026, 2:19 p.m.