Triple

T17204996
Position Surface form Disambiguated ID Type / Status
Subject State University of Padang E417576 entity
Predicate city P40 FINISHED
Object Padang NE NERFINISHED

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: Padang | Statement: [State University of Padang, city, Padang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Padang
Context triple: [State University of Padang, city, Padang]
  • A. Padang chosen
    Padang is a major coastal city in western Indonesia known as the capital of West Sumatra and a cultural and culinary center of the Minangkabau people.
  • B. Padang Panjang
    Padang Panjang is a small highland city in West Sumatra, Indonesia, known for its Minangkabau cultural heritage and cool mountainous climate.
  • C. Padang Besar
    Padang Besar is a border town in northern Malaysia known as a key land gateway and trading hub between Malaysia and Thailand.
  • D. Pekanbaru
    Pekanbaru is a major commercial and transportation hub in central Sumatra, Indonesia, known for its oil industry and rapid urban growth.
  • E. Padang Sidempuan
    Padang Sidempuan is a city in western Indonesia known as a regional center in the southern part of North Sumatra province.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d886d6ba8c819093215917b3d01689 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42dc0bea8819090946615a14b2d86 completed April 19, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:38 a.m.