Triple

T15890199
Position Surface form Disambiguated ID Type / Status
Subject Port of Kendari E385296 entity
Predicate locatedIn P40 FINISHED
Object Kendari E80839 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: Kendari | Statement: [Port of Kendari, locatedIn, Kendari]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kendari
Context triple: [Port of Kendari, locatedIn, Kendari]
  • A. Kendari chosen
    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.
  • 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. Tondano
    Tondano is a town in North Sulawesi, Indonesia, known as an administrative and cultural center of the Minahasa region near Lake Tondano.
  • D. Palopo
    Palopo is a coastal city in Indonesia known as an important regional center in the province of South Sulawesi.
  • E. 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.
  • 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_69d86da5b800819083a31be937d738b0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1561d5c28819094c3541d917a4433 completed April 16, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbc2cd84819080a90d983cd4d1a5 completed May 10, 2026, 1:13 a.m.
Created at: April 10, 2026, 4:51 a.m.