Triple

T9406512
Position Surface form Disambiguated ID Type / Status
Subject Chin State E226600 entity
Predicate hasTown P847 FINISHED
Object Kanpetlet
Kanpetlet is a small, remote town in western Myanmar known as a gateway to Nat Ma Taung (Mount Victoria) and its surrounding national park.
 E797242 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: Kanpetlet | Statement: [Chin State, hasTown, Kanpetlet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kanpetlet
Context triple: [Chin State, hasTown, Kanpetlet]
  • A. Kuto-Kute
    Kuto-Kute is a regional dialect of the Sasak language spoken by Sasak communities on the island of Lombok in Indonesia.
  • B. Kato Zakros
    Kato Zakros is a coastal village and archaeological site on the eastern coast of Crete, known for the remains of a significant Minoan palace complex.
  • C. Tikkana
    Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
  • D. Kagel
    Kagel is a small village in the municipality of Grünheide (Mark) in the Oder-Spree district of Brandenburg, Germany.
  • E. Tapa
    Tapa is a town in northern Estonia that serves as a key railway junction and transport hub in the country’s rail network.
  • 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: Kanpetlet
Triple: [Chin State, hasTown, Kanpetlet]
Generated description
Kanpetlet is a small, remote town in western Myanmar known as a gateway to Nat Ma Taung (Mount Victoria) and its surrounding national park.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kanpetlet
Target entity description: Kanpetlet is a small, remote town in western Myanmar known as a gateway to Nat Ma Taung (Mount Victoria) and its surrounding national park.
  • A. Kuto-Kute
    Kuto-Kute is a regional dialect of the Sasak language spoken by Sasak communities on the island of Lombok in Indonesia.
  • B. Kato Zakros
    Kato Zakros is a coastal village and archaeological site on the eastern coast of Crete, known for the remains of a significant Minoan palace complex.
  • C. Tikkana
    Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
  • D. Kagel
    Kagel is a small village in the municipality of Grünheide (Mark) in the Oder-Spree district of Brandenburg, Germany.
  • E. Tapa
    Tapa is a town in northern Estonia that serves as a key railway junction and transport hub in the country’s rail network.
  • 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_69ca843280488190bc65600e843ef9e6 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd51c3fc988190ac34cc9e09f8ebfc completed April 1, 2026, 5:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1079bd644819081f9c8f25ff1c532 completed April 4, 2026, 12:44 p.m.
NEDg Description generation batch_69d1082f41b48190b8588bb986028f59 completed April 4, 2026, 12:46 p.m.
NED2 Entity disambiguation (via description) batch_69d108bab8c881909748ffbb4b23f4ba completed April 4, 2026, 12:48 p.m.
Created at: March 30, 2026, 7:47 p.m.