Triple

T5417995
Position Surface form Disambiguated ID Type / Status
Subject Karapan sapi E121177 entity
Predicate notableCityVenue P19642 FINISHED
Object Bangkalan E152431 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: Bangkalan | Statement: [Karapan sapi, notableCityVenue, Bangkalan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bangkalan
Context triple: [Karapan sapi, notableCityVenue, Bangkalan]
  • A. Bangkalan chosen
    Bangkalan is a major urban center and regency on the western part of Madura Island in Indonesia, serving as an important gateway between Madura and Java.
  • B. Pasir Gudang
    Pasir Gudang is an industrial port city in the state of Johor, Malaysia, known for its heavy industries and maritime activities along the Straits of Johor.
  • C. Pagar Alam
    Pagar Alam is a highland city in southern Sumatra, Indonesia, known for its cool climate, tea plantations, and scenic mountain landscapes near Mount Dempo.
  • D. Padang Panjang
    Padang Panjang is a small highland city in West Sumatra, Indonesia, known for its Minangkabau cultural heritage and cool mountainous climate.
  • E. Bantaeng Regency
    Bantaeng Regency is an administrative region on the southern coast of Sulawesi, Indonesia, known for its agricultural economy and growing tourism sector.
  • 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_69bd463a41cc8190b32ff5af2b96ca93 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd91ae18cc8190aefe610f91b5382c completed March 20, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf3aadfa4c81908b57af80f534b121 completed March 22, 2026, 12:41 a.m.
Created at: March 20, 2026, 2:05 p.m.