Triple

T13464358
Position Surface form Disambiguated ID Type / Status
Subject Gowa Regency E311456 entity
Predicate contains P35 FINISHED
Object Malino
Malino is a cool highland town in South Sulawesi, Indonesia, known for its pine forests, tea plantations, and role as a popular mountain retreat.
 E1052471 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: Malino | Statement: [Gowa Regency, contains, Malino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Malino
Context triple: [Gowa Regency, contains, Malino]
  • A. Kidapawan
    Kidapawan is a city in the Philippines that serves as the capital of Cotabato province on the island of Mindanao.
  • B. Kabankalan
    Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
  • C. Bansalan
    Bansalan is a municipality in the province of Davao del Sur in the Philippines, known for its agricultural economy and rural communities.
  • D. Itogon
    Itogon is a mountainous municipality in Benguet province in the Philippines, known for its mining industry and scenic river valleys.
  • E. Bacolor
    Bacolor is a historic municipality in the Philippine province of Pampanga, known for its cultural heritage and for being heavily affected by the 1991 Mount Pinatubo eruption.
  • 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: Malino
Triple: [Gowa Regency, contains, Malino]
Generated description
Malino is a cool highland town in South Sulawesi, Indonesia, known for its pine forests, tea plantations, and role as a popular mountain retreat.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Malino
Target entity description: Malino is a cool highland town in South Sulawesi, Indonesia, known for its pine forests, tea plantations, and role as a popular mountain retreat.
  • A. Kidapawan
    Kidapawan is a city in the Philippines that serves as the capital of Cotabato province on the island of Mindanao.
  • B. Kabankalan
    Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
  • C. Bansalan
    Bansalan is a municipality in the province of Davao del Sur in the Philippines, known for its agricultural economy and rural communities.
  • D. Itogon
    Itogon is a mountainous municipality in Benguet province in the Philippines, known for its mining industry and scenic river valleys.
  • E. Bacolor
    Bacolor is a historic municipality in the Philippine province of Pampanga, known for its cultural heritage and for being heavily affected by the 1991 Mount Pinatubo eruption.
  • 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_69d806a938b8819097ec43a2229fc7f9 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf0f1830819085700b4521e44678 completed April 12, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ad5c68881908264947993bc7811 completed May 3, 2026, 5:50 p.m.
NEDg Description generation batch_69f78bd316788190a245e8199f6ac87b completed May 3, 2026, 5:54 p.m.
NED2 Entity disambiguation (via description) batch_69f78c94da6c8190b9bc1d04cee19c3c completed May 3, 2026, 5:57 p.m.
Created at: April 9, 2026, 9:41 p.m.