Triple

T19607525
Position Surface form Disambiguated ID Type / Status
Subject Desire E470645 entity
Predicate includesSingle P11236 FINISHED
Object Mozambique 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: Mozambique | Statement: [Desire, includesSingle, Mozambique]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mozambique
Context triple: [Desire, includesSingle, Mozambique]
  • A. Mozambique chosen
    Mozambique is a southeastern African nation on the Indian Ocean known for its Portuguese colonial heritage, rich cultural diversity, and extensive coastline with important ports and marine resources.
  • B. Malawi
    Malawi is a landlocked country in southeastern Africa known for Lake Malawi, its predominantly agricultural economy, and membership in regional and international organizations including the Commonwealth.
  • C. Malawi and Mozambique
    Malawi and Mozambique are neighboring countries in southeastern Africa that share a border traversed by the Shire River.
  • D. Malaweg
    Malaweg is a Philippine language of northern Luzon, considered a variety or closely related member of the Ibanag language group.
  • E. Tanzania
    Tanzania is an East African nation known for its vast wilderness areas, including the Serengeti National Park and Mount Kilimanjaro, as well as its rich cultural diversity.
  • 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_69d8e510fa248190b7afb274a1d4cf73 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e640c8aae8819086337e364724f1cb completed April 20, 2026, 3:05 p.m.
Created at: April 10, 2026, 1:43 p.m.