Triple

T8477847
Position Surface form Disambiguated ID Type / Status
Subject Brunswick, Germany E200439 entity
Predicate twinCity P1072 FINISHED
Object Bandung, Indonesia E22754 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: Bandung, Indonesia | Statement: [Brunswick, Germany, twinCity, Bandung, Indonesia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bandung, Indonesia
Context triple: [Brunswick, Germany, twinCity, Bandung, Indonesia]
  • A. Bandung chosen
    Bandung is a large Indonesian city on the island of Java known for its cool climate, universities, colonial and art deco architecture, and role as a center of culture and technology.
  • B. Purwakarta, Indonesia
    Purwakarta, Indonesia is a regency in West Java known as an industrial and manufacturing hub, including major automotive production facilities.
  • C. Cimahi
    Cimahi is an urban city in Indonesia located near Bandung in the province of West Java, known historically as a military and training center.
  • D. Cileunyi
    Cileunyi is a suburban district on the eastern outskirts of Bandung in West Java, Indonesia, known as a growing residential and transit area within the Bandung metropolitan region.
  • E. Bogor
    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.
  • 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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe51ffab881908448aff899511f2c completed March 31, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a196ad48190b3887a2a0c43f87f completed April 2, 2026, 9:42 a.m.
Created at: March 30, 2026, 6:12 p.m.