Triple

T9537033
Position Surface form Disambiguated ID Type / Status
Subject Laïty Kama E230043 entity
Predicate workLocation P7 FINISHED
Object Senegal E36946 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: Senegal | Statement: [Laïty Kama, workLocation, Senegal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Senegal
Context triple: [Laïty Kama, workLocation, Senegal]
  • A. Senegal chosen
    Senegal is a West African country on the Atlantic coast known for its vibrant culture, historic role in transatlantic trade, and diverse coastal and Sahelian landscapes.
  • B. The Gambia
    The Gambia is a small West African country centered around the Gambia River, known for its diverse ecosystems, colonial history, and tourism-focused economy.
  • C. Senegambia
    Senegambia is a historical region in West Africa encompassing present-day Senegal and The Gambia, known for its rich cultural diversity, early Islamic influence, and role in trans-Saharan and Atlantic trade.
  • D. Mali
    Mali is a landlocked West African country known for its historic trading cities like Timbuktu, rich Sahelian culture, and significant role in the ancient Mali Empire.
  • E. Mauritania
    Mauritania is a Northwest African country on the Atlantic coast, known for its vast Saharan landscapes, mixed Arab-Berber and Sub-Saharan cultures, and significant iron ore resources.
  • 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_69ca847b1b3081908f72bc932c17cc41 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98ce884c8190a8b3c2dc7c73c2c9 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d161355e2c819099c8e6b974f97608 completed April 4, 2026, 7:06 p.m.
Created at: March 30, 2026, 8:01 p.m.