Triple

T1868304
Position Surface form Disambiguated ID Type / Status
Subject Río Muni E34972 entity
Predicate containsProvince P11085 FINISHED
Object Wele-Nzas
Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
E210688 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: Wele-Nzas | Statement: [Río Muni, containsProvince, Wele-Nzas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wele-Nzas
Context triple: [Río Muni, containsProvince, Wele-Nzas]
  • A. Ndowe
    Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
  • B. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • C. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • D. Nzebi
    Nzebi is a Bantu language spoken primarily by the Nzebi people of Gabon and recognized there as one of the national languages.
  • E. Ikwerre
    Ikwerre is a Niger-Congo language spoken primarily by the Ikwerre people in Rivers State, Nigeria, particularly in and around Port Harcourt.
  • 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: Wele-Nzas
Triple: [Río Muni, containsProvince, Wele-Nzas]
Generated description
Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Wele-Nzas
Target entity description: Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
  • A. Ndowe
    Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
  • B. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • C. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • D. Nzebi
    Nzebi is a Bantu language spoken primarily by the Nzebi people of Gabon and recognized there as one of the national languages.
  • E. Ikwerre
    Ikwerre is a Niger-Congo language spoken primarily by the Ikwerre people in Rivers State, Nigeria, particularly in and around Port Harcourt.
  • 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_69a88600b2f88190bc09303e68ab517e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb0b6ac108190921c197abc5ab5b5 completed March 7, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf5163d88190a41df4c8f4e196f0 completed March 8, 2026, 8:42 p.m.
NEDg Description generation batch_69ade3146c80819080503bb501abca3a completed March 8, 2026, 8:59 p.m.
NED2 Entity disambiguation (via description) batch_69ade37080fc81908aa4eaf9aded9e59 completed March 8, 2026, 9 p.m.
Created at: March 4, 2026, 7:34 p.m.