Triple

T1462247
Position Surface form Disambiguated ID Type / Status
Subject Siege of La Rochelle (1572–1573) E31538 entity
Predicate location P40 FINISHED
Object La Rochelle E56822 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: La Rochelle | Statement: [Siege of La Rochelle (1572–1573), location, La Rochelle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Rochelle
Context triple: [Siege of La Rochelle (1572–1573), location, La Rochelle]
  • A. La Rochelle chosen
    La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
  • B. Toulon
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • C. Nantes
    Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
  • D. Niort
    Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
  • E. Rennes
    Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c5b6e36c81909c47b2f7e66f17d7 completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69afce6ea6f88190b455439f73a02728 completed March 10, 2026, 7:55 a.m.
Created at: March 1, 2026, 8 p.m.