Triple

T7789373
Position Surface form Disambiguated ID Type / Status
Subject Phang Nga E187336 entity
Predicate contains P35 FINISHED
Object Kapong
Kapong is a district-level administrative area located within Phang Nga Province in southern Thailand.
 E693569 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: Kapong | Statement: [Phang Nga, contains, Kapong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kapong
Context triple: [Phang Nga, contains, Kapong]
  • A. Pekela
    Pekela is a municipality in the province of Groningen in the northeastern Netherlands, known for its rural character and historical peat colonies.
  • B. Taroa
    Taroa is the main settlement and administrative center of Maloelap Atoll in the Marshall Islands, known historically for its World War II-era Japanese airbase.
  • C. Rana
    Rana is a large municipality in Nordland county, Norway, known for the town of Mo i Rana and its dramatic fjords, mountains, and caves just south of the Arctic Circle.
  • D. Rana
    Rana was the hereditary royal title borne by the ruling dynasty of the former princely state of Porbandar in western India.
  • E. Rana
    Rana is a large and widespread genus of true frogs that includes many familiar pond and stream-dwelling species found across much of the world.
  • 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: Kapong
Triple: [Phang Nga, contains, Kapong]
Generated description
Kapong is a district-level administrative area located within Phang Nga Province in southern Thailand.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kapong
Target entity description: Kapong is a district-level administrative area located within Phang Nga Province in southern Thailand.
  • A. Pekela
    Pekela is a municipality in the province of Groningen in the northeastern Netherlands, known for its rural character and historical peat colonies.
  • B. Taroa
    Taroa is the main settlement and administrative center of Maloelap Atoll in the Marshall Islands, known historically for its World War II-era Japanese airbase.
  • C. Rana
    Rana is a large municipality in Nordland county, Norway, known for the town of Mo i Rana and its dramatic fjords, mountains, and caves just south of the Arctic Circle.
  • D. Rana
    Rana was the hereditary royal title borne by the ruling dynasty of the former princely state of Porbandar in western India.
  • E. Rana
    Rana is a large and widespread genus of true frogs that includes many familiar pond and stream-dwelling species found across much of the world.
  • 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_69ca82af2d2c8190963861f5e0b8bf21 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cae7ea13f08190a60c5f1863bce816 completed March 30, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69caf62c8568819090c058b0c55b7865 completed March 30, 2026, 10:16 p.m.
NEDg Description generation batch_69caf820b05481908b405048c077ca2e completed March 30, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69cafa052d4481908b89f6001aa6ea01 completed March 30, 2026, 10:32 p.m.
Created at: March 30, 2026, 4:25 p.m.