Triple

T11841575
Position Surface form Disambiguated ID Type / Status
Subject Korean kingdoms E281664 entity
Predicate includes P1393 FINISHED
Object Buyeo E488614 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: Buyeo | Statement: [Korean kingdoms, includes, Buyeo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buyeo
Context triple: [Korean kingdoms, includes, Buyeo]
  • A. Buyeo chosen
    Buyeo was an ancient Korean kingdom that emerged in northern Manchuria and northern Korea, playing a key role in the early formation of Korean states and culture.
  • B. Yeongju
    Yeongju is a city in eastern South Korea known for its historic temples, Confucian academies, and scenic mountainous landscapes.
  • C. Meiktila
    Meiktila is a city in central Myanmar that served as a key strategic location during World War II, particularly in the Burma Campaign.
  • D. Seogwipo
    Seogwipo is a coastal city on South Korea’s Jeju Island known for its waterfalls, volcanic landscapes, and popular tourist attractions.
  • E. Hanseong
    Hanseong was the historical name for Seoul when it served as the capital of the Joseon Dynasty in Korea.
  • 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_69d6ab287ba48190a5178779fd19b9b7 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a658f918819092c2db05fe2ab0ce completed April 10, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69f1678668ac81909bddf67e8c176757 completed April 29, 2026, 2:05 a.m.
Created at: April 8, 2026, 9:43 p.m.