Triple

T20046931
Position Surface form Disambiguated ID Type / Status
Subject Belitung Regency E497583 entity
Predicate containsSettlement P847 FINISHED
Object Manggar
Manggar is a coastal town on Belitung Island in Indonesia, known for its tin mining history and numerous traditional coffee shops.
 E1409227 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: Manggar | Statement: [Belitung Regency, containsSettlement, Manggar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Manggar
Context triple: [Belitung Regency, containsSettlement, Manggar]
  • A. Manggala
    Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
  • B. Maranunggo
    Maranunggo is an alternative name for the Marranunggu, an Aboriginal Australian people traditionally associated with the Northern Territory.
  • C. Nanggu
    Nanggu is an Oceanic language spoken by a small community in the Solomon Islands, known for its distinct phonology and limited number of speakers.
  • D. Mangseng
    Mangseng is an Oceanic language spoken in parts of western Melanesia, belonging to the Western Oceanic branch of the Austronesian language family.
  • E. Pagaruyung
    Pagaruyung is a historic royal city in West Sumatra that served as the seat of the Minangkabau kingdom and remains an important symbol of Minangkabau culture and heritage.
  • 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: Manggar
Triple: [Belitung Regency, containsSettlement, Manggar]
Generated description
Manggar is a coastal town on Belitung Island in Indonesia, known for its tin mining history and numerous traditional coffee shops.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Manggar
Target entity description: Manggar is a coastal town on Belitung Island in Indonesia, known for its tin mining history and numerous traditional coffee shops.
  • A. Manggala
    Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
  • B. Maranunggo
    Maranunggo is an alternative name for the Marranunggu, an Aboriginal Australian people traditionally associated with the Northern Territory.
  • C. Nanggu
    Nanggu is an Oceanic language spoken by a small community in the Solomon Islands, known for its distinct phonology and limited number of speakers.
  • D. Mangseng
    Mangseng is an Oceanic language spoken in parts of western Melanesia, belonging to the Western Oceanic branch of the Austronesian language family.
  • E. Pagaruyung
    Pagaruyung is a historic royal city in West Sumatra that served as the seat of the Minangkabau kingdom and remains an important symbol of Minangkabau culture and heritage.
  • 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_69da627278c88190babe4297a9df1236 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6632b2de48190abe2b277d89eb695 completed April 20, 2026, 5:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a081603b36c8190b82688c3235fc794 completed May 16, 2026, 7 a.m.
NEDg Description generation batch_6a08180e5e988190a846aef83802cfc3 completed May 16, 2026, 7:09 a.m.
NED2 Entity disambiguation (via description) batch_6a0818970bb081908093589df4fc65b6 completed May 16, 2026, 7:11 a.m.
Created at: April 11, 2026, 3:37 p.m.