Triple

T8875360
Position Surface form Disambiguated ID Type / Status
Subject Allschwil E211264 entity
Predicate hasTwinTown P919 FINISHED
Object Pfullendorf E527602 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: Pfullendorf | Statement: [Allschwil, hasTwinTown, Pfullendorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pfullendorf
Context triple: [Allschwil, hasTwinTown, Pfullendorf]
  • A. Pfullendorf chosen
    Pfullendorf is a historic town in the state of Baden-Württemberg in southern Germany, known for its well-preserved medieval old town.
  • B. Pfullingen
    Pfullingen is a small town in the state of Baden-Württemberg in southwestern Germany, situated near the Swabian Jura and close to the city of Reutlingen.
  • C. Lülsfeld
    Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • D. Pennenfeld
    Pennenfeld is a residential subdistrict of the Bonn borough of Bad Godesberg in Germany.
  • E. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • 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_69ca838e78748190934d82db3104f855 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc614565788190aa14535760df88c8 completed April 1, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69d181fc06c48190b6a7444d975b1e09 completed April 4, 2026, 9:26 p.m.
Created at: March 30, 2026, 6:52 p.m.