Triple

T8404052
Position Surface form Disambiguated ID Type / Status
Subject East Jakarta E198448 entity
Predicate hasNotableArea P494 FINISHED
Object Makasar
Makasar is a district in East Jakarta, Indonesia, known as a primarily residential and urban area within the capital’s eastern region.
 E737477 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: Makasar | Statement: [East Jakarta, hasNotableArea, Makasar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Makasar
Context triple: [East Jakarta, hasNotableArea, Makasar]
  • A. Baubau
    Baubau is a coastal city in Southeast Sulawesi, Indonesia, known as a cultural and historical center of the Wolio-speaking Butonese people.
  • B. Makassar
    Makassar is a major port city on the southwest coast of Sulawesi known historically as a key maritime trading hub in eastern Indonesia.
  • C. Ternate
    Ternate is a coastal municipality in the province of Cavite in the Philippines, known for its beaches and historical significance.
  • D. Ternate
    Ternate is a small volcanic island and city in eastern Indonesia that was historically a major center of the global spice trade, especially for cloves.
  • E. Tarakan
    Tarakan is an island off the northeastern coast of Borneo in Indonesia, historically significant for its oil resources and as a strategic battleground during World War II.
  • 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: Makasar
Triple: [East Jakarta, hasNotableArea, Makasar]
Generated description
Makasar is a district in East Jakarta, Indonesia, known as a primarily residential and urban area within the capital’s eastern region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Makasar
Target entity description: Makasar is a district in East Jakarta, Indonesia, known as a primarily residential and urban area within the capital’s eastern region.
  • A. Baubau
    Baubau is a coastal city in Southeast Sulawesi, Indonesia, known as a cultural and historical center of the Wolio-speaking Butonese people.
  • B. Makassar
    Makassar is a major port city on the southwest coast of Sulawesi known historically as a key maritime trading hub in eastern Indonesia.
  • C. Ternate
    Ternate is a coastal municipality in the province of Cavite in the Philippines, known for its beaches and historical significance.
  • D. Ternate
    Ternate is a small volcanic island and city in eastern Indonesia that was historically a major center of the global spice trade, especially for cloves.
  • E. Tarakan
    Tarakan is an island off the northeastern coast of Borneo in Indonesia, historically significant for its oil resources and as a strategic battleground during World War II.
  • 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_69ca8310df9c8190b25f16161cca3e41 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb82505e0c81909549db59b7c4eb00 completed March 31, 2026, 8:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce397e169c8190b2decf637b422e6c completed April 2, 2026, 9:40 a.m.
NEDg Description generation batch_69ce3d5cda908190887b8c38ef0cc1e2 completed April 2, 2026, 9:56 a.m.
NED2 Entity disambiguation (via description) batch_69ce3dc22c3c8190b8b2c396a74f5911 completed April 2, 2026, 9:58 a.m.
Created at: March 30, 2026, 6:04 p.m.