Triple

T1053706
Position Surface form Disambiguated ID Type / Status
Subject Bandung E22754 entity
Predicate nickname P55 FINISHED
Object Kota Kembang E29215 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: Kota Kembang | Statement: [Bandung, nickname, Kota Kembang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kota Kembang
Context triple: [Bandung, nickname, Kota Kembang]
  • A. Cirebon
    Cirebon is a coastal city in West Java, Indonesia, known as a cultural crossroads blending Sundanese and Javanese influences and serving as a significant regional trading and urban center.
  • B. Bogor chosen
    Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
  • C. Tasikmalaya
    Tasikmalaya is a significant city in West Java, Indonesia, known as an important cultural and economic hub for the Sundanese people.
  • D. Surakarta
    Surakarta is a historic Javanese city in Central Java, Indonesia, renowned as a traditional cultural center and royal court city closely associated with classical arts such as gamelan music and dance.
  • E. Bukittinggi
    Bukittinggi is a historic highland city in West Sumatra, Indonesia, renowned as a major hub of Minangkabau culture, history, and tourism.
  • 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_69a493da02e081908c13ff5e02a0fe7a completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b8d669448190955507e2e4975b9f completed March 1, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac93ad5644819095868c520c33cd83 completed March 7, 2026, 9:07 p.m.
Created at: March 1, 2026, 7:42 p.m.