Triple

T17281681
Position Surface form Disambiguated ID Type / Status
Subject Autobahn A5 E419543 entity
Predicate hasJunctionWith P1018 FINISHED
Object Autobahn A45 NE NERFINISHED

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: Autobahn A45 | Statement: [Autobahn A5, hasJunctionWith, Autobahn A45]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Autobahn A45
Context triple: [Autobahn A5, hasJunctionWith, Autobahn A45]
  • A. Autobahn A45 chosen
    Autobahn A45 is a major German motorway running through western Germany, connecting the Ruhr area with central and southern regions and serving as an important north–south transport corridor.
  • B. Autobahn A44
    Autobahn A44 is a major German motorway in western Germany that links key cities and transport hubs, including providing direct access to Düsseldorf Airport.
  • C. Autobahn A43
    Autobahn A43 is a German federal motorway in North Rhine-Westphalia that connects the Ruhr area with the Münsterland region.
  • D. Autobahn A46
    Autobahn A46 is a German federal motorway in North Rhine-Westphalia that connects several major cities in the Rhine-Ruhr metropolitan region.
  • E. Autobahn A40
    Autobahn A40 is a major east–west motorway in western Germany that runs through the Ruhr area and is known for its heavy traffic and congestion.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d886da626481908a14ce7830329a35 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e4332a4c008190b44f4145d0e94a21 completed April 19, 2026, 1:43 a.m.
Created at: April 10, 2026, 5:40 a.m.