Triple

T10035934
Position Surface form Disambiguated ID Type / Status
Subject Wantage E204968 entity
Predicate hasTwinTown P919 FINISHED
Object Seesen E239870 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: Seesen | Statement: [Wantage, hasTwinTown, Seesen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seesen
Context triple: [Wantage, hasTwinTown, Seesen]
  • A. Seesen chosen
    Seesen is a town in Lower Saxony, Germany, located on the northwestern edge of the Harz mountains and known for its historical architecture and regional cultural significance.
  • B. Ehringshausen
    Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
  • C. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • D. Niederschöneweide
    Niederschöneweide is a locality in the Berlin borough of Treptow-Köpenick, known for its riverside setting along the Spree and its mix of residential areas and former industrial sites.
  • E. Breckerfeld
    Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
  • 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_69ca834d77188190ad645e33e8ca3200 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdce4bb3408190ac5dae4718ef7cad completed April 2, 2026, 2:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d28258ab088190a31ad5854d91193b completed April 5, 2026, 3:40 p.m.
Created at: March 30, 2026, 8:55 p.m.