Triple

T20257970
Position Surface form Disambiguated ID Type / Status
Subject Baekje Historic Areas E498753 entity
Predicate locatedIn P40 FINISHED
Object Buyeo 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: Buyeo | Statement: [Baekje Historic Areas, locatedIn, Buyeo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buyeo
Context triple: [Baekje Historic Areas, locatedIn, Buyeo]
  • A. Buyeo
    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. Pakchon
    Pakchon is a city in North Pyongan Province, North Korea, known as a regional center for agriculture and light industry.
  • C. Buyeo-eup chosen
    Buyeo-eup is a town in South Chungcheong Province, South Korea, known for its rich Baekje-era heritage and numerous historical and archaeological sites.
  • D. Yeongju
    Yeongju is a city in eastern South Korea known for its historic temples, Confucian academies, and scenic mountainous landscapes.
  • E. Meiktila
    Meiktila is a city in central Myanmar that served as a key strategic location during World War II, particularly in the Burma Campaign.
  • 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_69da6275fa6c8190952924930adee150 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e674c7296c819092860942de8f28d5 completed April 20, 2026, 6:47 p.m.
Created at: April 11, 2026, 11:41 p.m.