Triple

T20962700
Position Surface form Disambiguated ID Type / Status
Subject Kiel Hauptbahnhof E516286 entity
Predicate hasNameInEnglish P3437 FINISHED
Object Kiel Central Station E516286 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: Kiel Central Station | Statement: [Kiel Hauptbahnhof, hasNameInEnglish, Kiel Central Station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kiel Central Station
Context triple: [Kiel Hauptbahnhof, hasNameInEnglish, Kiel Central Station]
  • A. Kiel Hauptbahnhof chosen
    Kiel Hauptbahnhof is the main railway station and central transportation hub serving the port city of Kiel in northern Germany.
  • B. Hamburg Central Station
    Hamburg Central Station is the main railway hub of Hamburg and one of Germany’s busiest train stations, serving as a key national and international transport interchange.
  • C. Eidelstedt station
    Eidelstedt station is a railway and S-Bahn station in the Eidelstedt district of Hamburg, Germany, serving as a local and regional transport hub.
  • D. Wende station
    Wende station is a metro station on Taipei's Wenhu (Brown) Line serving the Neihu District in Taiwan.
  • E. Kiest Station
    Kiest Station is a Dallas Area Rapid Transit (DART) light rail stop on the Blue Line serving the Kiest Boulevard area in Dallas, Texas.
  • 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_69e0b4fde6c48190af1398e7e734629e completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fb710d6c8190a2daacaad3b22683 completed April 21, 2026, 4:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a092798800481909d437b58467f8e2b completed May 17, 2026, 2:27 a.m.
Created at: April 16, 2026, 1:32 p.m.