Triple

T11890456
Position Surface form Disambiguated ID Type / Status
Subject 2016 European floods E282900 entity
Predicate countryAffected P1059 FINISHED
Object France E861 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: France | Statement: [2016 European floods, countryAffected, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: France
Context triple: [2016 European floods, countryAffected, France]
  • A. France chosen
    France is a major Western European nation known for its influential history, culture, and economy, and as a founding member of the European Union and the United Nations.
  • B. Francia
    Francia is the surname of American rower and two-time Olympic gold medalist Susan Francia.
  • C. Pays Royannais
    Pays Royannais is a coastal area in southwestern France centered around the town of Royan, known for its seaside resorts and Atlantic beaches.
  • D. État français
    État français was the authoritarian regime led by Marshal Philippe Pétain that governed unoccupied France from Vichy during World War II and collaborated with Nazi Germany.
  • E. Lafrançaise, France
    Lafrançaise is a small commune in the Tarn-et-Garonne department of southern France, known for its rural charm and traditional French village atmosphere.
  • 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_69d6ab2a90b08190a4e818821cc93e6d completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8d3a3f7548190adfb567f060a175a completed April 10, 2026, 10:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69f417f7f268819091bdb72394506808 completed May 1, 2026, 3:03 a.m.
Created at: April 8, 2026, 9:44 p.m.