Triple

T16919793
Position Surface form Disambiguated ID Type / Status
Subject Rudow E410414 entity
Predicate borderedBy P224 FINISHED
Object Schönefeld E180745 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: Schönefeld | Statement: [Rudow, borderedBy, Schönefeld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Schönefeld
Context triple: [Rudow, borderedBy, Schönefeld]
  • A. Schönefeld chosen
    Schönefeld is a municipality just southeast of Berlin in the German state of Brandenburg, known for hosting the Berlin Brandenburg Airport.
  • B. Berlin Schönefeld Airport
    Berlin Schönefeld Airport was Berlin’s former secondary international airport located southeast of the city, which was eventually incorporated into the new Berlin Brandenburg Airport complex.
  • C. Tegel
    Tegel is a locality in the Reinickendorf borough of Berlin, Germany, historically known for its manor associated with the Humboldt family and later for the former Berlin Tegel Airport.
  • D. Berlin Brandenburg Airport
    Berlin Brandenburg Airport is the main international airport serving Germany’s capital region, designed to replace and consolidate Berlin’s former commercial airports.
  • E. Dessau Airport
    Dessau Airport is a small regional airfield in Dessau, Germany, primarily serving general aviation and historical aviation activities.
  • 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_6a00c7c2c9cc8190b6d59d0a8edd078d completed May 10, 2026, 6 p.m.
Created at: April 10, 2026, 5:30 a.m.