Triple

T2960236
Position Surface form Disambiguated ID Type / Status
Subject Port Moresby E80027 entity
Predicate hasSubdivision P747 FINISHED
Object Konedobu
Konedobu is a suburb of Port Moresby in Papua New Guinea, known for housing many government offices and administrative facilities.
 E313882 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: Konedobu | Statement: [Port Moresby, hasSubdivision, Konedobu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Konedobu
Context triple: [Port Moresby, hasSubdivision, Konedobu]
  • A. Kono
    Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
  • B. Kono
    Kono is a major Mande language spoken primarily in parts of West Africa, notably in Sierra Leone and neighboring regions.
  • C. Kibushi
    Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
  • D. Machimura
    Machimura is a Japanese surname most notably associated with Nobutaka Machimura, a prominent Liberal Democratic Party politician and former foreign minister of Japan.
  • E. Takamikura
    Takamikura is the ornate imperial throne used in Kyoto for the enthronement ceremonies of Japanese emperors.
  • 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: Konedobu
Triple: [Port Moresby, hasSubdivision, Konedobu]
Generated description
Konedobu is a suburb of Port Moresby in Papua New Guinea, known for housing many government offices and administrative facilities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Konedobu
Target entity description: Konedobu is a suburb of Port Moresby in Papua New Guinea, known for housing many government offices and administrative facilities.
  • A. Kono
    Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
  • B. Kono
    Kono is a major Mande language spoken primarily in parts of West Africa, notably in Sierra Leone and neighboring regions.
  • C. Kibushi
    Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
  • D. Machimura
    Machimura is a Japanese surname most notably associated with Nobutaka Machimura, a prominent Liberal Democratic Party politician and former foreign minister of Japan.
  • E. Takamikura
    Takamikura is the ornate imperial throne used in Kyoto for the enthronement ceremonies of Japanese emperors.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad992dd4248190b5f3d4f342593b8c completed March 8, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc923d888190a68075dfaa9e90b2 completed March 11, 2026, 5:24 a.m.
NEDg Description generation batch_69b0fd7d1cc88190a4f533a92d7e6de3 completed March 11, 2026, 5:28 a.m.
NED2 Entity disambiguation (via description) batch_69b0fde74b608190b59da720c90adfeb completed March 11, 2026, 5:30 a.m.
Created at: March 8, 2026, 2:57 p.m.