Triple

T13272552
Position Surface form Disambiguated ID Type / Status
Subject Governor of West Sulawesi E316100 entity
Predicate seat P75 FINISHED
Object Mamuju E316096 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: Mamuju | Statement: [Governor of West Sulawesi, seat, Mamuju]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mamuju
Context triple: [Governor of West Sulawesi, seat, Mamuju]
  • A. Mamuju chosen
    Mamuju is a coastal city on the island of Sulawesi in Indonesia known as an administrative and economic center in the region.
  • B. Gunsan
    Gunsan is a coastal city in North Jeolla Province, South Korea, known for its port, industrial facilities, and longstanding association with nearby military air operations.
  • C. Yeoju
    Yeoju is a city in South Korea known for its rich historical heritage, including royal tombs and ceramics, and its scenic riverside landscapes.
  • D. Gijeon
    Gijeon is an alternative name for the Seoul Capital Area, the densely populated metropolitan region surrounding South Korea’s capital city.
  • E. Mokpo
    Mokpo is a coastal city in South Jeolla Province, South Korea, known as a regional transportation hub and gateway to numerous nearby islands.
  • 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_69d806b1d9ac8190852c5571d5bd5f0f completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99020f710819094c2618662bdc7fd completed April 11, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69f74610b2c481909497296e999226e8 completed May 3, 2026, 12:56 p.m.
Created at: April 9, 2026, 9:26 p.m.