Triple

T10490447
Position Surface form Disambiguated ID Type / Status
Subject Kunama languages E247402 entity
Predicate ISO639-3Code P208 FINISHED
Object kun (macro-language)
Kun (macro-language) is an ISO 639-3 macrolanguage code that collectively represents the Kunama languages spoken primarily in parts of Eritrea and neighboring regions.
E867525 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: kun (macro-language) | Statement: [Kunama languages, ISO639-3Code, kun (macro-language)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: kun (macro-language)
Context triple: [Kunama languages, ISO639-3Code, kun (macro-language)]
  • A. KUN
    KUN is the IATA airport code for Kaunas Airport, a commercial international airport serving the city of Kaunas in Lithuania.
  • B. KÜN
    KÜN is the vehicle registration code for the German district of Hohenlohekreis in the state of Baden-Württemberg.
  • C. Kun
    Kun is an alternative name for the Cumans, a historically significant nomadic Turkic people who roamed the Eurasian steppes during the Middle Ages.
  • D. Kun
    Kun is a prominent high-altitude mountain peak in the Indian Himalayas, known as one of the major summits of the Nun-Kun massif in the Ladakh region.
  • E. KLEX
    KLEX is the ICAO airport code for Blue Grass Airport, a public airport serving Lexington, Kentucky, in the United States.
  • 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: kun (macro-language)
Triple: [Kunama languages, ISO639-3Code, kun (macro-language)]
Generated description
Kun (macro-language) is an ISO 639-3 macrolanguage code that collectively represents the Kunama languages spoken primarily in parts of Eritrea and neighboring regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: kun (macro-language)
Target entity description: Kun (macro-language) is an ISO 639-3 macrolanguage code that collectively represents the Kunama languages spoken primarily in parts of Eritrea and neighboring regions.
  • A. KUN
    KUN is the IATA airport code for Kaunas Airport, a commercial international airport serving the city of Kaunas in Lithuania.
  • B. KÜN
    KÜN is the vehicle registration code for the German district of Hohenlohekreis in the state of Baden-Württemberg.
  • C. Kun
    Kun is an alternative name for the Cumans, a historically significant nomadic Turkic people who roamed the Eurasian steppes during the Middle Ages.
  • D. Kun
    Kun is a prominent high-altitude mountain peak in the Indian Himalayas, known as one of the major summits of the Nun-Kun massif in the Ladakh region.
  • E. KLEX
    KLEX is the ICAO airport code for Blue Grass Airport, a public airport serving Lexington, Kentucky, in the United States.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5097d61e08190952d4354ef1bce52 completed April 7, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8dc9792308190b09d6aaed63dd418 completed April 10, 2026, 11:18 a.m.
NEDg Description generation batch_69d8e8c81bdc8190b6b6dfe00025b514 completed April 10, 2026, 12:10 p.m.
NED2 Entity disambiguation (via description) batch_69d901ef24608190934377d9dc855d6f completed April 10, 2026, 1:58 p.m.
Created at: April 6, 2026, 12:23 p.m.