Triple

T12411989
Position Surface form Disambiguated ID Type / Status
Subject Matmut Atlantique E296538 entity
Predicate sponsor P67 FINISHED
Object Matmut E982620 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: Matmut | Statement: [Matmut Atlantique, sponsor, Matmut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matmut
Context triple: [Matmut Atlantique, sponsor, Matmut]
  • A. Matmut chosen
    Matmut is a French mutual insurance company that provides a wide range of insurance and financial services to individuals and businesses.
  • B. Mutsamudu
    Mutsamudu is the main urban and economic center of the Comorian island of Anjouan, known for its historic medina and Indian Ocean port.
  • C. Mamati
    Mamati is a village in western Georgia notable as the birthplace of former Soviet and Georgian politician Eduard Shevardnadze.
  • D. Banzebi
    Banzebi are a subgroup of the Nzebi people, an ethnic community primarily found in Central Africa, especially in Gabon and surrounding regions.
  • E. Baaka
    Baaka is the traditional Aboriginal name for the Darling River, one of the major inland rivers of southeastern Australia.
  • 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_69d6ad9f464c81909db36d7e96e34b9e completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d6b0f9c8190813b6fe3f97570ac completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63efe60388190944fe3226be4cc7c completed May 2, 2026, 6:14 p.m.
Created at: April 8, 2026, 9:55 p.m.