Triple

T2867623
Position Surface form Disambiguated ID Type / Status
Subject Albert Schweitzer E63477 entity
Predicate placeOfDeath P21 FINISHED
Object Lambaréné E245957 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: Lambaréné | Statement: [Albert Schweitzer, placeOfDeath, Lambaréné]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lambaréné
Context triple: [Albert Schweitzer, placeOfDeath, Lambaréné]
  • A. Lambaréné chosen
    Lambaréné is a town in western Gabon best known for its location on the Ogooué River and for hosting the historic Albert Schweitzer Hospital.
  • B. Pointe-Noire
    Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial centers.
  • C. Matadi
    Matadi is a major port city in western Democratic Republic of the Congo, serving as the country’s principal seaport and a key gateway for trade between the Atlantic Ocean and the interior via the Congo River.
  • D. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • E. Bangui
    Bangui is the capital and largest city of the Central African Republic, serving as its political, economic, and cultural center.
  • 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_69ab4c42fb8c8190b36e161d47c03b81 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdfbcebcc81909a78a1787d823e3e completed March 7, 2026, 8:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69b01da85930819092d19a5e712cfa18 completed March 10, 2026, 1:33 p.m.
Created at: March 6, 2026, 10:02 p.m.