Triple

T2408513
Position Surface form Disambiguated ID Type / Status
Subject Alfred Clebsch E50330 entity
Predicate workLocation P7 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: [Alfred Clebsch, workLocation, Giessen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Giessen
Context triple: [Alfred Clebsch, workLocation, 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. Darmstadt
    Darmstadt is a city in the German state of Hesse known for its historical ties to the Grand Duchy of Hesse and its role as a center of science, technology, and Art Nouveau culture.
  • 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_69a88b0339a88190a1207333cd271cc9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc92408308190ad2d331ebee71d15 completed March 7, 2026, 6:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69badb06ddac8190a70d72b491de59c8 completed March 18, 2026, 5:04 p.m.
Created at: March 4, 2026, 7:58 p.m.