Triple

T539982
Position Surface form Disambiguated ID Type / Status
Subject Strasbourg E12607 entity
Predicate historicalName P65 FINISHED
Object Straßburg E12607 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: Straßburg | Statement: [Strasbourg, historicalName, Straßburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Straßburg
Context triple: [Strasbourg, historicalName, Straßburg]
  • A. Strasbourg chosen
    Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
  • B. Mulhouse
    Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
  • C. Metz
    Metz is a historic city in northeastern France known for its Gothic Saint-Stephen Cathedral, Roman and medieval heritage, and role as the capital of the Moselle department in the Grand Est region.
  • D. Trier
    Trier is a historic city in western Germany, renowned as one of the country’s oldest cities with extensive Roman ruins and medieval landmarks.
  • E. Besançon
    Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
  • 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_69a49334226c81908b0ea1689ef6aa3f completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4985e51908190a34aa82ea9dbee1e completed March 1, 2026, 7:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d6571588190b1f328e7b10b627c completed March 3, 2026, 11:23 p.m.
Created at: March 1, 2026, 7:32 p.m.