Triple

T10933968
Position Surface form Disambiguated ID Type / Status
Subject Askunu E258279 entity
Predicate hasAlternativeName P39 FINISHED
Object Askunu language
Askunu language is an Indo-Iranian language spoken by the Ashkun people in parts of eastern Afghanistan.
 E893718 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: Askunu language | Statement: [Askunu, hasAlternativeName, Askunu language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Askunu language
Context triple: [Askunu, hasAlternativeName, Askunu language]
  • A. Kaxabu language
    The Kaxabu language is an indigenous Formosan language of Taiwan spoken by the Kaxabu people and considered highly endangered.
  • B. Anuak language
    The Anuak language is a Nilotic language spoken primarily by the Anuak people of western Ethiopia and eastern South Sudan.
  • C. Akassa language
    The Akassa language is a Niger-Congo language spoken by the Akassa people of Nigeria’s Niger Delta and is part of the Ijaw language group.
  • D. Äynu language
    The Äynu language is a rare mixed Turkic–Iranian language spoken primarily by the Äynu ethnic minority in Xinjiang, China.
  • E. Avokaya language
    The Avokaya language is a Central Sudanic language spoken primarily by the Avokaya people in parts of South Sudan and the Democratic Republic of the Congo.
  • 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: Askunu language
Triple: [Askunu, hasAlternativeName, Askunu language]
Generated description
Askunu language is an Indo-Iranian language spoken by the Ashkun people in parts of eastern Afghanistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Askunu language
Target entity description: Askunu language is an Indo-Iranian language spoken by the Ashkun people in parts of eastern Afghanistan.
  • A. Kaxabu language
    The Kaxabu language is an indigenous Formosan language of Taiwan spoken by the Kaxabu people and considered highly endangered.
  • B. Anuak language
    The Anuak language is a Nilotic language spoken primarily by the Anuak people of western Ethiopia and eastern South Sudan.
  • C. Akassa language
    The Akassa language is a Niger-Congo language spoken by the Akassa people of Nigeria’s Niger Delta and is part of the Ijaw language group.
  • D. Äynu language
    The Äynu language is a rare mixed Turkic–Iranian language spoken primarily by the Äynu ethnic minority in Xinjiang, China.
  • E. Avokaya language
    The Avokaya language is a Central Sudanic language spoken primarily by the Avokaya people in parts of South Sudan and the Democratic Republic of the Congo.
  • 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_69d6aa8769b4819082bfe5e61b9017f0 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770ae073881909720febe9f5f296a completed April 9, 2026, 9:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2176328448190bbce6735ec97507a completed April 17, 2026, 11:20 a.m.
NEDg Description generation batch_69e21d8aea2881908ac8f5225b8739c5 completed April 17, 2026, 11:46 a.m.
NED2 Entity disambiguation (via description) batch_69e21eb18a1881908ded331db89063ed completed April 17, 2026, 11:51 a.m.
Created at: April 8, 2026, 9:23 p.m.