Triple

T2129354
Position Surface form Disambiguated ID Type / Status
Subject A35 E46500 entity
Predicate servesCity P82 FINISHED
Object Sélestat E57547 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: Sélestat | Statement: [A35, servesCity, Sélestat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sélestat
Context triple: [A35, servesCity, Sélestat]
  • A. Sélestat chosen
    Sélestat is a historic town in the Alsace region of northeastern France, known for its well-preserved medieval architecture and cultural heritage.
  • B. Molsheim
    Molsheim is a historic town in northeastern France’s Grand Est region, known for its medieval architecture and as the birthplace of the Bugatti automobile brand.
  • C. Haguenau
    Haguenau is a historic town in northeastern France’s Alsace region, known for its medieval heritage, cultural traditions, and role as a local economic center.
  • D. Schiltigheim
    Schiltigheim is a suburban commune in northeastern France, located just north of Strasbourg and known historically for its brewing industry.
  • E. Furtwangen
    Furtwangen is a small town in Germany’s Black Forest region, historically known for its clockmaking industry and home to the Furtwangen University of Applied Sciences.
  • 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_69a88a1626548190ae59a5028c3baa8e completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbb77ccc4819087bee5dbb91b5ae8 completed March 7, 2026, 5:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae51a36398819081df18cc18bc3456 completed March 9, 2026, 4:50 a.m.
Created at: March 4, 2026, 7:44 p.m.