Triple

T1151325
Position Surface form Disambiguated ID Type / Status
Subject Tanjung Perak E23683 entity
Predicate locatedIn P40 FINISHED
Object Surabaya E9894 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: Surabaya | Statement: [Tanjung Perak, locatedIn, Surabaya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Surabaya
Context triple: [Tanjung Perak, locatedIn, Surabaya]
  • A. Surabaya chosen
    Surabaya is Indonesia’s second-largest city and a key commercial and industrial hub on the island of Java, historically serving as one of the region’s most important seaports.
  • B. 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.
  • C. Surakarta
    Surakarta is a historic Javanese city in Central Java, Indonesia, renowned as a traditional cultural center and royal court city closely associated with classical arts such as gamelan music and dance.
  • D. Pasuruan
    Pasuruan is a city in East Java, Indonesia, known as a gateway to the popular Mount Bromo volcanic tourism area.
  • E. Cirebon
    Cirebon is a coastal city in West Java, Indonesia, known as a cultural crossroads blending Sundanese and Javanese influences and serving as a significant regional trading and urban 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc744e7c81908f8612f2aad28600 completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc61497948190891228f1a623668e completed March 8, 2026, 12:43 a.m.
Created at: March 1, 2026, 7:44 p.m.