Triple

T8858867
Position Surface form Disambiguated ID Type / Status
Subject Marie Louise of France E210832 entity
Predicate birthPlace P1 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: [Marie Louise of France, birthPlace, Versailles, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Versailles, France
Context triple: [Marie Louise of France, birthPlace, 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 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.
  • C. 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.
  • D. 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.
  • E. Fontainebleau, France
    Fontainebleau, France is a historic town southeast of Paris best known for its vast forest and royal château, long associated with French monarchs and outdoor recreation.
  • 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_69ca838bbddc8190ab546d737e5d350f completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60e536648190ba8da1375478c24f completed April 1, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfa0b1b86481909ec0b78de043d8f8 completed April 3, 2026, 11:12 a.m.
Created at: March 30, 2026, 6:50 p.m.