Triple

T20954483
Position Surface form Disambiguated ID Type / Status
Subject Petals of Blood E516063 entity
Predicate hasCharacter P2308 FINISHED
Object Karega
Karega is a central character in Ngũgĩ wa Thiong’o’s novel "Petals of Blood," portrayed as a radical teacher and activist who challenges postcolonial injustice in Kenya.
 E1459555 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: Karega | Statement: [Petals of Blood, hasCharacter, Karega]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Karega
Context triple: [Petals of Blood, hasCharacter, Karega]
  • A. Kandia
    Kandia is a remote valley and settlement area located within Pakistan’s Kohistan mountain ranges, known for its rugged terrain and isolated communities.
  • B. Karume
    Karume is a Swahili surname most prominently associated with Abeid Karume, the first president of Zanzibar and a key figure in Tanzanian political history.
  • C. Kabaena
    Kabaena is an island in Indonesia known for its location off the coast of Sulawesi and its mix of coastal and hilly landscapes.
  • D. Buhera
    Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
  • E. Kinyara
    Kinyara is a town in Uganda’s Masindi District, best known for its large sugar estate and associated agro-industrial activities.
  • 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: Karega
Triple: [Petals of Blood, hasCharacter, Karega]
Generated description
Karega is a central character in Ngũgĩ wa Thiong’o’s novel "Petals of Blood," portrayed as a radical teacher and activist who challenges postcolonial injustice in Kenya.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Karega
Target entity description: Karega is a central character in Ngũgĩ wa Thiong’o’s novel "Petals of Blood," portrayed as a radical teacher and activist who challenges postcolonial injustice in Kenya.
  • A. Kandia
    Kandia is a remote valley and settlement area located within Pakistan’s Kohistan mountain ranges, known for its rugged terrain and isolated communities.
  • B. Karume
    Karume is a Swahili surname most prominently associated with Abeid Karume, the first president of Zanzibar and a key figure in Tanzanian political history.
  • C. Kabaena
    Kabaena is an island in Indonesia known for its location off the coast of Sulawesi and its mix of coastal and hilly landscapes.
  • D. Buhera
    Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
  • E. Kinyara
    Kinyara is a town in Uganda’s Masindi District, best known for its large sugar estate and associated agro-industrial activities.
  • 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_69e0b4fcd678819087a304291f14330a completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fae2299c8190afa1b9ec5bd9df32 completed April 21, 2026, 4:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0927926a4881908a3aa2a088c2b2e1 completed May 17, 2026, 2:27 a.m.
NEDg Description generation batch_6a0929b7db588190825b4c8b3aeacd6e completed May 17, 2026, 2:36 a.m.
NED2 Entity disambiguation (via description) batch_6a092a2cd49c8190a9ee196d62d1bb1c completed May 17, 2026, 2:38 a.m.
Created at: April 16, 2026, 1:28 p.m.