Triple

T3876255
Position Surface form Disambiguated ID Type / Status
Subject North Sumatra E92507 entity
Predicate hasMajorCity P316 FINISHED
Object Pematangsiantar E400884 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: Pematangsiantar | Statement: [North Sumatra, hasMajorCity, Pematangsiantar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pematangsiantar
Context triple: [North Sumatra, hasMajorCity, Pematangsiantar]
  • A. Pematangsiantar chosen
    Pematangsiantar is a major city in North Sumatra, Indonesia, known as an important economic and transportation hub in the region.
  • B. Padang Sidempuan
    Padang Sidempuan is a city in western Indonesia known as a regional center in the southern part of North Sumatra province.
  • C. Tanjungbalai
    Tanjungbalai is a coastal city and port in northeastern Sumatra, Indonesia, known for its fishing industry and location along the Asahan River.
  • D. Binjai
    Binjai is a city in Indonesia located near Medan on the island of Sumatra, known as a regional trade and transit hub.
  • E. Padang Panjang
    Padang Panjang is a small highland city in West Sumatra, Indonesia, known for its Minangkabau cultural heritage and cool mountainous climate.
  • 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_69aed967448c819086c4b358d37b25aa completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec706434819095e0d2b376adb548 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b39f55c819099e5ce90137de570 completed March 14, 2026, 2:05 p.m.
Created at: March 9, 2026, 3:20 p.m.