Triple

T2253823
Position Surface form Disambiguated ID Type / Status
Subject Chris Rock E49674 entity
Predicate notableWork P4 FINISHED
Object Madagascar E27635 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: Madagascar | Statement: [Chris Rock, notableWork, Madagascar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madagascar
Context triple: [Chris Rock, notableWork, Madagascar]
  • A. Madagascar chosen
    Madagascar is a large island nation in the Indian Ocean renowned for its unique biodiversity and high rate of endemic species.
  • B. Mauritius
    Mauritius is an island nation in the Indian Ocean known for its multicultural society, stable democracy, and tourism-driven economy.
  • C. Seychelles
    Seychelles is an Indian Ocean island nation off the coast of East Africa, known for its tropical beaches, coral reefs, and unique biodiversity.
  • D. Socotra
    Socotra is a remote Yemeni island renowned for its unique biodiversity and otherworldly landscapes, including the iconic dragon’s blood trees.
  • E. Comoros
    Comoros is an island nation in the Indian Ocean off the eastern coast of Africa, known for its diverse cultural heritage and history as a former French colony.
  • 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_69a88aaa9250819095e127d0d77e8a32 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc12029548190af9f2cdd7a4de2d6 completed March 7, 2026, 6:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69af989e2cf48190b9c5d053491e3a96 completed March 10, 2026, 4:05 a.m.
Created at: March 4, 2026, 7:47 p.m.