Triple

T8375496
Position Surface form Disambiguated ID Type / Status
Subject Central Germany E197565 entity
Predicate containsCity P294 FINISHED
Object Giessen E264892 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: Giessen | Statement: [Central Germany, containsCity, Giessen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Giessen
Context triple: [Central Germany, containsCity, Giessen]
  • A. Gießen chosen
    Gießen is a mid-sized university city in central Germany known for its academic institutions and role as a regional administrative and cultural center.
  • B. Kassel
    Kassel is a city in central Germany known for its cultural institutions and as the host of the renowned contemporary art exhibition documenta.
  • C. Wetzlar
    Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
  • D. Heilbronn
    Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
  • E. Heppenheim
    Heppenheim is a historic town in southwestern Germany, known for its picturesque old town, vineyards, and location on the Bergstraße at the edge of the Odenwald.
  • 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_69ca82f56730819080cec5d991c76f4c completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80bf6b8081909b98762b1f900bef completed March 31, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0c696913c819089f29ec899d4ee61 completed April 4, 2026, 8:06 a.m.
Created at: March 30, 2026, 6:01 p.m.