Triple

T8367218
Position Surface form Disambiguated ID Type / Status
Subject Fana Mokoena E197158 entity
Predicate givenName P17 FINISHED
Object Fana
Fana is a South African actor and politician known for his roles in films such as "Hotel Rwanda" and "World War Z."
 E731233 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: Fana | Statement: [Fana Mokoena, givenName, Fana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fana
Context triple: [Fana Mokoena, givenName, Fana]
  • A. Kwaluudhi
    Kwaluudhi is a dialect of the Ovambo language spoken by a specific Ovambo subgroup in northern Namibia.
  • B. Baafira
    Baafira is a popular dancehall/reggae song by Ghanaian artist Stonebwoy that helped boost his prominence in the African music scene.
  • C. Qunya
    Qunya is a historic town in central Anatolia, in present-day Turkey, known as the birthplace of the prominent Sufi philosopher Sadr al-Din al-Qunawi.
  • D. Xola
    Xola is a Mexico City Metro station on Line 2 that serves the southern part of the city near the Calzada de Tlalpan corridor.
  • E. Nyanda
    Nyanda is the former name of Masvingo, a historic city in southeastern Zimbabwe known for its proximity to the Great Zimbabwe ruins.
  • 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: Fana
Triple: [Fana Mokoena, givenName, Fana]
Generated description
Fana is a South African actor and politician known for his roles in films such as "Hotel Rwanda" and "World War Z."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fana
Target entity description: Fana is a South African actor and politician known for his roles in films such as "Hotel Rwanda" and "World War Z."
  • A. Kwaluudhi
    Kwaluudhi is a dialect of the Ovambo language spoken by a specific Ovambo subgroup in northern Namibia.
  • B. Baafira
    Baafira is a popular dancehall/reggae song by Ghanaian artist Stonebwoy that helped boost his prominence in the African music scene.
  • C. Qunya
    Qunya is a historic town in central Anatolia, in present-day Turkey, known as the birthplace of the prominent Sufi philosopher Sadr al-Din al-Qunawi.
  • D. Xola
    Xola is a Mexico City Metro station on Line 2 that serves the southern part of the city near the Calzada de Tlalpan corridor.
  • E. Nyanda
    Nyanda is the former name of Masvingo, a historic city in southeastern Zimbabwe known for its proximity to the Great Zimbabwe ruins.
  • 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_69ca82f2dbe48190aba982e75a0d94de completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb808e56fc81908b5d37482f29452d completed March 31, 2026, 8:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce02aab4488190abc63bace296e32a completed April 2, 2026, 5:46 a.m.
NEDg Description generation batch_69ce077d8af0819082a7ea67a2c11ddd completed April 2, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_69ce082078108190867044f45bc0a806 completed April 2, 2026, 6:09 a.m.
Created at: March 30, 2026, 6 p.m.