Triple

T14554015
Position Surface form Disambiguated ID Type / Status
Subject Songo E341491 entity
Predicate country P26 FINISHED
Object Mozambique E13411 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: Mozambique | Statement: [Songo, country, Mozambique]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mozambique
Context triple: [Songo, country, 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 (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_69d822db9c8481908213ceb39585f792 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb2f00cec8190a7b6482d18b9a216 completed April 14, 2026, 9:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8ab9a5ac81908779a3c8701353fa completed May 8, 2026, 7:03 a.m.
Created at: April 10, 2026, 1:23 a.m.