Triple

T6217331
Position Surface form Disambiguated ID Type / Status
Subject Macassar E139021 entity
Predicate formerNameOf P65 FINISHED
Object Makassar E23614 NE FINISHED

How this triple was built (2 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: Makassar | Statement: [Macassar, formerNameOf, Makassar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Makassar
Context triple: [Macassar, formerNameOf, Makassar]
  • A. Makassar chosen
    Makassar is a major port city on the southwest coast of Sulawesi known historically as a key maritime trading hub in eastern Indonesia.
  • B. Kendari
    Kendari is the capital and largest city of Southeast Sulawesi Province on the Indonesian island of Sulawesi, known as a regional center for trade and maritime activities.
  • C. Palu
    Palu is a coastal city on the Indonesian island of Sulawesi, known as the capital of Central Sulawesi province and a regional center for trade and administration.
  • D. Banjarmasin
    Banjarmasin is a major riverine city in South Kalimantan, Indonesia, known for its historic floating markets and strategic location on the island of Borneo.
  • E. Palopo
    Palopo is a coastal city in Indonesia known as an important regional center in the province of South Sulawesi.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c008aecb0c81909984b48f733ce8ae completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c062a35e308190be25c41b02704411 completed March 22, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c67c36eac4819095b6a2b9e8a277f0 completed March 27, 2026, 12:46 p.m.
Created at: March 22, 2026, 4:21 p.m.