Triple

T7358734
Position Surface form Disambiguated ID Type / Status
Subject Java economic region E169691 entity
Predicate hasMajorCity P316 FINISHED
Object Bandung 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 | Statement: [Java economic region, hasMajorCity, Bandung]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bandung
Context triple: [Java economic region, hasMajorCity, Bandung]
  • 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. 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.
  • C. Bandung metropolitan area
    The Bandung metropolitan area is a major urban and economic region in West Java, Indonesia, centered on the city of Bandung and its surrounding suburbs and satellite towns.
  • D. Tasikmalaya
    Tasikmalaya is a significant city in West Java, Indonesia, known as an important cultural and economic hub for the Sundanese people.
  • E. 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.
  • 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_69c68a59f2288190877ca15c19b1e822 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f13db3488190ad35725c4fc60ffe completed March 27, 2026, 9:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69c802a61718819088cc2d37818af4ca completed March 28, 2026, 4:32 p.m.
Created at: March 27, 2026, 3:06 p.m.