Triple

T15945456
Position Surface form Disambiguated ID Type / Status
Subject Hadiyya language E386671 entity
Predicate isAlsoKnownAs P39 FINISHED
Object Hadiyya Afoo
Hadiyya Afoo is a Cushitic language spoken primarily by the Hadiyya people in southern Ethiopia.
E1186077 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: Hadiyya Afoo | Statement: [Hadiyya language, isAlsoKnownAs, Hadiyya Afoo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hadiyya Afoo
Context triple: [Hadiyya language, isAlsoKnownAs, Hadiyya Afoo]
  • A. Rashidah
    Rashidah is a feminine given name of Arabic origin commonly used in Muslim communities.
  • B. Mizzi Ahmar
    Mizzi Ahmar is a reddish variety of Jerusalem stone commonly used as a traditional building material in and around Jerusalem.
  • C. Fahdah
    Fahdah is a Saudi princess, formally known as Princess Fahdah Mohammed Abunayyan, associated with the Saudi royal family.
  • D. Hawa Abdi
    Hawa Abdi was a pioneering Somali doctor, human rights activist, and humanitarian who founded a hospital and refuge for tens of thousands of displaced people during her country’s civil war.
  • E. Haya Harareet
    Haya Harareet was an Israeli actress best known for her role as Esther opposite Charlton Heston in the 1959 epic film "Ben-Hur."
  • 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: Hadiyya Afoo
Triple: [Hadiyya language, isAlsoKnownAs, Hadiyya Afoo]
Generated description
Hadiyya Afoo is a Cushitic language spoken primarily by the Hadiyya people in southern Ethiopia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Hadiyya Afoo
Target entity description: Hadiyya Afoo is a Cushitic language spoken primarily by the Hadiyya people in southern Ethiopia.
  • A. Rashidah
    Rashidah is a feminine given name of Arabic origin commonly used in Muslim communities.
  • B. Mizzi Ahmar
    Mizzi Ahmar is a reddish variety of Jerusalem stone commonly used as a traditional building material in and around Jerusalem.
  • C. Fahdah
    Fahdah is a Saudi princess, formally known as Princess Fahdah Mohammed Abunayyan, associated with the Saudi royal family.
  • D. Hawa Abdi
    Hawa Abdi was a pioneering Somali doctor, human rights activist, and humanitarian who founded a hospital and refuge for tens of thousands of displaced people during her country’s civil war.
  • E. Haya Harareet
    Haya Harareet was an Israeli actress best known for her role as Esther opposite Charlton Heston in the 1959 epic film "Ben-Hur."
  • 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_69d86da882448190a82ea962fe343b79 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d0d55c8190af59ff169e8add78 completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe76df6481909f8246099faa377a completed May 9, 2026, 11:08 p.m.
NEDg Description generation batch_69ffbf61b2ec81909c0f32613bb91a82 completed May 9, 2026, 11:12 p.m.
NED2 Entity disambiguation (via description) batch_69ffbfddd0348190baab794f613c71bf completed May 9, 2026, 11:14 p.m.
Created at: April 10, 2026, 4:53 a.m.