Triple

T7973363
Position Surface form Disambiguated ID Type / Status
Subject Tangerang E185381 entity
Predicate alsoKnownAs P39 FINISHED
Object Kota Tangerang E185381 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 Tangerang | Statement: [Tangerang, alsoKnownAs, Kota Tangerang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kota Tangerang
Context triple: [Tangerang, alsoKnownAs, Kota Tangerang]
  • A. Tangerang chosen
    Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
  • B. Serang
    Serang is the capital city of Banten Province on the western tip of Java, Indonesia, serving as an important regional administrative and economic center.
  • C. Cilegon
    Cilegon is an industrial port city in western Java, Indonesia, known for its steel industry and strategic location near the Sunda Strait.
  • D. South Tangerang
    South Tangerang is a rapidly developing satellite city in Indonesia’s Banten province, known as a residential and commercial hub within the Jakarta metropolitan area.
  • E. Bekasi
    Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
  • 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_69ca829851908190b4e03829353ee7c3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3bf319648190a900b133d58bd02b completed March 31, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe0bb089881909d3ec17a3330ce25 completed March 31, 2026, 2:56 p.m.
Created at: March 30, 2026, 5:13 p.m.