Triple

T9694523
Position Surface form Disambiguated ID Type / Status
Subject Araucanian languages E234613 entity
Predicate hasPart P35 FINISHED
Object Chedungun
Chedungun is a variety of the Mapuche (Araucanian) language spoken by the Mapuche people of south-central Chile and neighboring regions of Argentina.
E815820 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: Chedungun | Statement: [Araucanian languages, hasPart, Chedungun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chedungun
Context triple: [Araucanian languages, hasPart, Chedungun]
  • A. Sungsang
    Sungsang is a coastal village in South Sumatra, Indonesia, known as a fishing and port settlement near the mouth of the Musi River.
  • B. Junggumun
    Junggumun is a historical writing system used in Korea that incorporated Chinese characters to represent Korean grammatical elements and sounds.
  • C. Conchan
    Conchan is the historic parish name for the area now known as Onchan on the Isle of Man.
  • D. Donggureung
    Donggureung is a large royal burial complex in Guri, South Korea, containing multiple tombs of Joseon Dynasty kings and queens and recognized as part of a UNESCO World Heritage site.
  • E. Gulgong
    Gulgong is a historic gold rush town in New South Wales, Australia, known for its well-preserved 19th-century streetscapes and heritage buildings.
  • 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: Chedungun
Triple: [Araucanian languages, hasPart, Chedungun]
Generated description
Chedungun is a variety of the Mapuche (Araucanian) language spoken by the Mapuche people of south-central Chile and neighboring regions of Argentina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Chedungun
Target entity description: Chedungun is a variety of the Mapuche (Araucanian) language spoken by the Mapuche people of south-central Chile and neighboring regions of Argentina.
  • A. Sungsang
    Sungsang is a coastal village in South Sumatra, Indonesia, known as a fishing and port settlement near the mouth of the Musi River.
  • B. Junggumun
    Junggumun is a historical writing system used in Korea that incorporated Chinese characters to represent Korean grammatical elements and sounds.
  • C. Conchan
    Conchan is the historic parish name for the area now known as Onchan on the Isle of Man.
  • D. Donggureung
    Donggureung is a large royal burial complex in Guri, South Korea, containing multiple tombs of Joseon Dynasty kings and queens and recognized as part of a UNESCO World Heritage site.
  • E. Gulgong
    Gulgong is a historic gold rush town in New South Wales, Australia, known for its well-preserved 19th-century streetscapes and heritage buildings.
  • 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d348868819083aec7a5da8c455b completed April 1, 2026, 10:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1912216d481909f6a0f977d570a93 completed April 4, 2026, 10:30 p.m.
NEDg Description generation batch_69d1926e9154819086be60bd6fa55453 completed April 4, 2026, 10:36 p.m.
NED2 Entity disambiguation (via description) batch_69d19611bbe08190b1893727c1fa05ce completed April 4, 2026, 10:52 p.m.
Created at: March 30, 2026, 8:17 p.m.