Triple

T7627342
Position Surface form Disambiguated ID Type / Status
Subject Bekasi E172668 entity
Predicate borderedBy P224 FINISHED
Object Bekasi Regency E645265 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: Bekasi Regency | Statement: [Bekasi, borderedBy, Bekasi Regency]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bekasi Regency
Context triple: [Bekasi, borderedBy, Bekasi Regency]
  • A. Bekasi Regency chosen
    Bekasi Regency is an administrative region in West Java, Indonesia, known for its rapidly growing urban and industrial areas on the eastern outskirts of Jakarta.
  • B. Bekasi
    Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
  • C. Bogor Regency
    Bogor Regency is an administrative region in West Java, Indonesia, that encircles the city of Bogor and is known for its rapidly growing suburban and rural communities.
  • D. Depok
    Depok is a rapidly growing commuter city in Indonesia located between Jakarta and Bogor, known for its universities and residential developments.
  • E. Purwakarta Regency
    Purwakarta Regency is an administrative region in West Java, Indonesia, known for its industrial areas, transportation links, and proximity to major urban centers like Bandung and Jakarta.
  • 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_69c699517e348190bd3348b6889200f2 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6fa8150ac8190908aec411b0f4e50 completed March 27, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69c89ab1132481909e525e90764df041 completed March 29, 2026, 3:21 a.m.
Created at: March 27, 2026, 3:56 p.m.