Triple

T3060654
Position Surface form Disambiguated ID Type / Status
Subject Greater Surabaya metropolitan area E60587 entity
Predicate hasComponent P35 FINISHED
Object Madiun City E182493 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: Madiun City | Statement: [Greater Surabaya metropolitan area, hasComponent, Madiun City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madiun City
Context triple: [Greater Surabaya metropolitan area, hasComponent, Madiun City]
  • A. Madiun chosen
    Madiun is a city in eastern Java, Indonesia, known as a regional economic and transportation hub with a strong railway and agricultural industry presence.
  • B. Pasuruan
    Pasuruan is a city in East Java, Indonesia, known as a gateway to the popular Mount Bromo volcanic tourism area.
  • C. Kediri
    Kediri is a historic city in Indonesia known for its role as a former Javanese kingdom center and as an important economic hub in modern East Java.
  • D. Magetan
    Magetan is a regency and town in East Java, Indonesia, known for its cool climate, agricultural production, and proximity to the scenic Sarangan Lake and Mount Lawu.
  • E. Malang
    Malang is a major city in East Java, Indonesia, known for its cool climate, colonial-era architecture, and proximity to popular mountain and volcanic tourist destinations.
  • 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_69ad8578137c81908259dcb27c7d6d7c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ad9e9e1e248190b5ed5ebcdad1321e completed March 8, 2026, 4:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bb66b1248190917a7c3c146ec727 completed March 13, 2026, 7:23 a.m.
Created at: March 8, 2026, 3:02 p.m.