Triple

T3417065
Position Surface form Disambiguated ID Type / Status
Subject Borsigwerke E72033 entity
Predicate district P2709 FINISHED
Object Tegel E2522 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: Tegel | Statement: [Borsigwerke, district, Tegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tegel
Context triple: [Borsigwerke, district, Tegel]
  • A. Tegel chosen
    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.
  • B. Schönefeld
    Schönefeld is a municipality just southeast of Berlin in the German state of Brandenburg, known for hosting the Berlin Brandenburg Airport.
  • C. Tempelhof Airport
    Tempelhof Airport is a historic Berlin airfield best known as a central hub of the Berlin Airlift during the Cold War.
  • D. 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.
  • E. 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.
  • 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_69ad85ad38e48190b7660c5118a35289 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb92c20fc81909b5debced20ec083 completed March 8, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4881f1e1c81908de3a0729761c4f7 completed March 13, 2026, 9:56 p.m.
Created at: March 8, 2026, 3:15 p.m.