Triple

T35411590
Position Surface form Disambiguated ID Type / Status
Subject King of Goryeo E1023525 entity
Predicate titleHolder P1911 FINISHED
Object Gongmin of Goryeo
Gongmin of Goryeo was a 14th-century Korean monarch known for efforts to reform government corruption, reduce Mongol influence, and strengthen the Goryeo dynasty during a turbulent period.
E2174462 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: Gongmin of Goryeo | Statement: [King of Goryeo, titleHolder, Gongmin of Goryeo]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gongmin of Goryeo
Triple: [King of Goryeo, titleHolder, Gongmin of Goryeo]
Generated description
Gongmin of Goryeo was a 14th-century Korean monarch known for efforts to reform government corruption, reduce Mongol influence, and strengthen the Goryeo dynasty during a turbulent period.

Provenance (5 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_69f76df54bac8190bd0d3b0eb35cda5f completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f795672b40819087ccce744e044124 completed May 3, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a394d1606dc8190b251bbc728bcf3e9 completed June 22, 2026, 2:56 p.m.
NEDg Description generation batch_6a394e2f9fd881909f48adfb2bc5f64c completed June 22, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a394f6d294881909aa6bcd083c73e13 completed June 22, 2026, 3:06 p.m.
Created at: May 3, 2026, 4:03 p.m.