Triple

T10127297
Position Surface form Disambiguated ID Type / Status
Subject Karakalpak language E226246 entity
Predicate languageCode P15 FINISHED
Object kaa
Kaa is the ISO 639-3 language code for the Karakalpak language, a Turkic language spoken primarily in Karakalpakstan, Uzbekistan.
 E841569 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: kaa | Statement: [Karakalpak language, languageCode, kaa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: kaa
Context triple: [Karakalpak language, languageCode, kaa]
  • A. KAA
    KAA is the state corporation responsible for managing and operating Kenya’s civil airports and airstrips.
  • B. KAA
    KAA is the commonly used abbreviation for K.A.A. Gent, a professional football club based in Ghent, Belgium.
  • C. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • D. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • E. KAIA
    KAIA is a major international airport in Jeddah, Saudi Arabia, serving as a key gateway for pilgrims traveling to the holy cities of Mecca and Medina.
  • 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: kaa
Triple: [Karakalpak language, languageCode, kaa]
Generated description
Kaa is the ISO 639-3 language code for the Karakalpak language, a Turkic language spoken primarily in Karakalpakstan, Uzbekistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: kaa
Target entity description: Kaa is the ISO 639-3 language code for the Karakalpak language, a Turkic language spoken primarily in Karakalpakstan, Uzbekistan.
  • A. KAA
    KAA is the state corporation responsible for managing and operating Kenya’s civil airports and airstrips.
  • B. KAA
    KAA is the commonly used abbreviation for K.A.A. Gent, a professional football club based in Ghent, Belgium.
  • C. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • D. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • E. KAIA
    KAIA is a major international airport in Jeddah, Saudi Arabia, serving as a key gateway for pilgrims traveling to the holy cities of Mecca and Medina.
  • 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_69ca843057b48190a86730167f5d6b98 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cdd2eef7388190b95ffd02814f2d1f completed April 2, 2026, 2:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2cc69a5c88190ab7b108e1aab20ba completed April 5, 2026, 8:56 p.m.
NEDg Description generation batch_69d2cd90fb888190833ca68dd644860d completed April 5, 2026, 9:01 p.m.
NED2 Entity disambiguation (via description) batch_69d2ce02525881908a5394acb7967ba2 completed April 5, 2026, 9:02 p.m.
Created at: March 30, 2026, 9:05 p.m.