Triple

T2748745
Position Surface form Disambiguated ID Type / Status
Subject Juanda International Airport E60932 entity
Predicate locatedIn P40 FINISHED
Object Sidoarjo Regency E195838 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: Sidoarjo Regency | Statement: [Juanda International Airport, locatedIn, Sidoarjo Regency]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sidoarjo Regency
Context triple: [Juanda International Airport, locatedIn, Sidoarjo Regency]
  • A. Sidoarjo chosen
    Sidoarjo is a rapidly developing urban and industrial center in Indonesia known for its proximity to Surabaya and its significant role in East Java’s economy.
  • B. Mojokerto
    Mojokerto is a city in Indonesia known for its historical significance as part of the former Majapahit Empire and its location in the province of East Java.
  • C. Gresik
    Gresik is an industrial and port city in Indonesia known for its cement production and role as part of the Surabaya metropolitan area.
  • D. Bojonegoro
    Bojonegoro is a regency capital and regional economic center in the western part of East Java, Indonesia, known for its agriculture and oil and gas production.
  • E. Tulungagung
    Tulungagung is a regency and urban center in southern East Java, Indonesia, known for its marble industry and coastal landscapes along the Indian Ocean.
  • 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_69ab4b79846081909096725374d65ce9 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb517a00819084fd8f8933a25212 completed March 7, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbbd341a88190ae0f5eb94a6fdc92 completed March 10, 2026, 6:36 a.m.
Created at: March 6, 2026, 9:56 p.m.