Triple

T6098722
Position Surface form Disambiguated ID Type / Status
Subject Kaili language E135940 entity
Predicate hasDialect P4251 FINISHED
Object Palu E80399 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: Palu | Statement: [Kaili language, hasDialect, Palu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Palu
Context triple: [Kaili language, hasDialect, Palu]
  • A. Palu chosen
    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.
  • 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. Gorontalo City
    Gorontalo City is an urban center on the northern coast of Sulawesi, Indonesia, known as the administrative, economic, and cultural hub of the surrounding Gorontalo region.
  • D. Makassar
    Makassar is a major port city on the southwest coast of Sulawesi known historically as a key maritime trading hub in eastern Indonesia.
  • E. Balikpapan
    Balikpapan is a coastal city in East Kalimantan, Indonesia, known as a major oil and gas hub and one of the most developed urban centers on the island of Borneo.
  • 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_69c0087cd3c48190b459848c72d84eb1 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05a9a02888190ac201acd14c3fc31 completed March 22, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c673f2327481908f541d4b2095c958 completed March 27, 2026, 12:11 p.m.
Created at: March 22, 2026, 4:13 p.m.