Triple

T10560461
Position Surface form Disambiguated ID Type / Status
Subject Arquus E249204 entity
Predicate headquartersLocation P62 FINISHED
Object Versailles, France E9321 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: Versailles, France | Statement: [Arquus, headquartersLocation, Versailles, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Versailles, France
Context triple: [Arquus, headquartersLocation, Versailles, France]
  • A. Versailles
    Versailles is a small borough in Allegheny County, Pennsylvania, situated along the Youghiogheny River in the Pittsburgh metropolitan area.
  • B. Versailles
    "Versailles" is a historical drama television series centered on the reign of France's King Louis XIV and the political intrigue surrounding the construction and life at the Palace of Versailles.
  • C. Versailles chosen
    Versailles is a historic French city best known for the opulent Palace of Versailles, a former royal residence and a symbol of absolute monarchy and French cultural grandeur.
  • D. Ermont, France
    Ermont is a suburban commune in the northern outskirts of Paris, France, known for its residential character and transport links within the Val-d'Oise department.
  • E. Blois, France
    Blois, France is a historic city on the Loire River known for its Renaissance château and as the birthplace of King Stephen of England.
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5271f3c6c819080b49fbe3aa09e09 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d93486c7288190a2ccb822fc968919 completed April 10, 2026, 5:33 p.m.
Created at: April 6, 2026, 12:35 p.m.