Triple

T16919801
Position Surface form Disambiguated ID Type / Status
Subject Rudow E410414 entity
Predicate roadAccess P385 FINISHED
Object Bundesstraße 96a 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 96a | Statement: [Rudow, roadAccess, Bundesstraße 96a]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bundesstraße 96a
Context triple: [Rudow, roadAccess, Bundesstraße 96a]
  • 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 9
    Bundesstraße 9 is a major German federal highway running along the western part of the country, connecting numerous cities and towns near the Rhine.
  • C. Bundesstraße 64
    Bundesstraße 64 is a German federal highway that runs east–west through parts of Lower Saxony and North Rhine-Westphalia, connecting several regional towns and cities.
  • D. Bundesstraße 91
    Bundesstraße 91 is a German federal highway in the state of Saxony-Anhalt that connects the town of Weißenfels with other regional centers.
  • E. Bundesstraße 85
    Bundesstraße 85 is a major federal highway in Germany that runs through eastern and central regions, connecting numerous towns and cities across the country.
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3cded2f8481909a20cc08b47e922e completed April 18, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012ecafd908190b8a1513138a29303 completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:30 a.m.