Triple

T22665862
Position Surface form Disambiguated ID Type / Status
Subject WITA E559785 entity
Predicate appliesToRegion P82 FINISHED
Object Makassar NE NERFINISHED

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: [WITA, appliesToRegion, Makassar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Makassar
Context triple: [WITA, appliesToRegion, 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. Makasar
    Makasar is a district in East Jakarta, Indonesia, known as a primarily residential and urban area within the capital’s eastern region.
  • D. 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.
  • E. Palu
    Palu is a historic town and district in eastern Turkey known for its ancient ruins and location along the Murat River in Elazığ Province.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2454a158c819093b8e35f5045efb6 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1781c2c808190baf6964ca1eced6f completed April 29, 2026, 3:16 a.m.
Created at: April 17, 2026, 3:08 p.m.