Triple

T21635916
Position Surface form Disambiguated ID Type / Status
Subject MGA E533957 entity
Predicate isLegalTenderIn P2878 FINISHED
Object Madagascar NE NERFINISHED

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: Madagascar | Statement: [MGA, isLegalTenderIn, Madagascar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madagascar
Context triple: [MGA, isLegalTenderIn, Madagascar]
  • A. Madagascar
    Madagascar is a 2005 animated comedy film produced by DreamWorks Animation that follows a group of Central Park Zoo animals who find themselves stranded on the island of Madagascar.
  • B. Madagascar chosen
    Madagascar is a large island nation in the Indian Ocean renowned for its unique biodiversity and high rate of endemic species.
  • C. Mauritius
    Mauritius is an island nation in the Indian Ocean known for its multicultural society, stable democracy, and tourism-driven economy.
  • D. Mauricius
    Mauricius is a Latin given name of Roman origin that later evolved into various European forms such as Maurice and Morris.
  • E. Ambarikorano, Madagascar
    Ambarikorano, Madagascar is a locality in Madagascar known as the birthplace of the country’s first president, Philibert Tsiranana.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c465ae7481908577b7209fdb2a77 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef538c5fbc8190a3316cf91f9516dc completed April 27, 2026, 12:16 p.m.
Created at: April 16, 2026, 6:35 p.m.