Triple

T907155
Position Surface form Disambiguated ID Type / Status
Subject Alsace E19573 entity
Predicate containsCity P294 FINISHED
Object Mulhouse E78039 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: Mulhouse | Statement: [Alsace, containsCity, Mulhouse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mulhouse
Context triple: [Alsace, containsCity, Mulhouse]
  • A. Mulhouse chosen
    Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
  • B. Strasbourg
    Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
  • C. 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.
  • D. Lancy
    Lancy is a suburban municipality in western Switzerland that forms part of the urban area of Geneva.
  • E. Thionville
    Thionville is a town in northeastern France near the Luxembourg border, known historically as a strategic industrial and military center in the Moselle region.
  • 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_69a4939e889c8190ac148b3ac1a7f90b completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2cc85a08190bc8186eb9ed1fa38 completed March 1, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf0949c481908868bd27eeb964b4 completed March 8, 2026, 12:12 a.m.
Created at: March 1, 2026, 7:39 p.m.