Triple

T3060644
Position Surface form Disambiguated ID Type / Status
Subject Greater Surabaya metropolitan area E60587 entity
Predicate hasComponent P35 FINISHED
Object Probolinggo City E135336 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: Probolinggo City | Statement: [Greater Surabaya metropolitan area, hasComponent, Probolinggo City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Probolinggo City
Context triple: [Greater Surabaya metropolitan area, hasComponent, Probolinggo City]
  • A. Probolinggo chosen
    Probolinggo is a coastal city in East Java, Indonesia, known as a common gateway for tourists visiting the Mount Bromo volcanic area.
  • B. Nganjuk
    Nganjuk is a regency capital and regional urban center in the province of East Java, Indonesia.
  • C. Pasuruan
    Pasuruan is a city in East Java, Indonesia, known as a gateway to the popular Mount Bromo volcanic tourism area.
  • D. Lamongan
    Lamongan is a regency and its capital town in the northern coastal region of East Java, Indonesia, known for its fishing industry and distinctive local cuisine.
  • E. Jombang
    Jombang is a regency-level town in Indonesia known as an important regional center in the province of East Java.
  • 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_69b354505574819083ae7d36e461366b completed March 13, 2026, 12:03 a.m.
Created at: March 8, 2026, 3:02 p.m.