Triple

T2539124
Position Surface form Disambiguated ID Type / Status
Subject Bata E56339 entity
Predicate locatedInRegion P40 FINISHED
Object Rio Muni
Rio Muni is the mainland region of Equatorial Guinea, located on the west coast of Central Africa and forming the country's largest and most populous territory.
 E281302 NE FINISHED

How this triple was built (4 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: Rio Muni | Statement: [Bata, locatedInRegion, Rio Muni]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rio Muni
Context triple: [Bata, locatedInRegion, Rio Muni]
  • A. Río de Oro
    Río de Oro was a former Spanish colonial territory in northwest Africa that later became part of the disputed region of Western Sahara.
  • B. Congo
    Congo is a Central African country whose economy is heavily reliant on oil production and exports.
  • C. Mékrou River
    The Mékrou River is a West African waterway that flows through Benin, Burkina Faso, and Niger, helping form part of the borders between these countries and contributing significantly to the Niger River basin.
  • 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. Kongō
    Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rio Muni
Triple: [Bata, locatedInRegion, Rio Muni]
Generated description
Rio Muni is the mainland region of Equatorial Guinea, located on the west coast of Central Africa and forming the country's largest and most populous territory.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rio Muni
Target entity description: Rio Muni is the mainland region of Equatorial Guinea, located on the west coast of Central Africa and forming the country's largest and most populous territory.
  • A. Río de Oro
    Río de Oro was a former Spanish colonial territory in northwest Africa that later became part of the disputed region of Western Sahara.
  • B. Congo
    Congo is a Central African country whose economy is heavily reliant on oil production and exports.
  • C. Mékrou River
    The Mékrou River is a West African waterway that flows through Benin, Burkina Faso, and Niger, helping form part of the borders between these countries and contributing significantly to the Niger River basin.
  • 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. Kongō
    Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
  • F. None of above. chosen

Provenance (5 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_69ab4a49b6508190bc467fbef4bac334 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd29b44448190ba4f82b0c1425f21 completed March 7, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69af839849288190a6ddf488f3f69203 completed March 10, 2026, 2:36 a.m.
NEDg Description generation batch_69af84c112b88190aa13deff23994f2b completed March 10, 2026, 2:41 a.m.
NED2 Entity disambiguation (via description) batch_69af8566eb9c8190b606122d5e67d7c8 completed March 10, 2026, 2:43 a.m.
Created at: March 6, 2026, 9:47 p.m.