Triple

T4247526
Position Surface form Disambiguated ID Type / Status
Subject Peugeot 205 E95563 entity
Predicate assemblyLocation P40 FINISHED
Object Mulhouse, France 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, France | Statement: [Peugeot 205, assemblyLocation, Mulhouse, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mulhouse, France
Context triple: [Peugeot 205, assemblyLocation, Mulhouse, France]
  • 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. Ribemont, France
    Ribemont, France is a small commune in northern France best known as the birthplace of Enlightenment philosopher and mathematician Marquis de Condorcet.
  • C. Sochaux, France
    Sochaux, France is an industrial town in eastern France best known as the historic home of the Peugeot automobile manufacturing complex.
  • D. Auxerre, France
    Auxerre, France is a historic city in the Burgundy region known for its medieval architecture, Gothic cathedral, and role as a cultural and economic center along the Yonne River.
  • E. Montreuil, France
    Montreuil is a suburban commune in the eastern part of the Paris metropolitan area, known for its diverse population and mix of residential, commercial, and cultural spaces.
  • 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_69b3453d91548190b4d4ef8fe52aa2ac completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e9cb71481909b4baa370193148f completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a87c033881908e0cf9fdfecaf36a completed March 14, 2026, 6:27 p.m.
Created at: March 12, 2026, 11:05 p.m.