Triple

T27847249
Position Surface form Disambiguated ID Type / Status
Subject The King of Pigs (TV series) E703856 entity
Predicate character P662 FINISHED
Object Jung Jong-suk
Jung Jong-suk is a fictional character in the South Korean thriller drama series "The King of Pigs," which explores themes of school bullying, trauma, and revenge.
E2288192 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: Jung Jong-suk | Statement: [The King of Pigs (TV series), character, Jung Jong-suk]
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: Jung Jong-suk
Triple: [The King of Pigs (TV series), character, Jung Jong-suk]
Generated description
Jung Jong-suk is a fictional character in the South Korean thriller drama series "The King of Pigs," which explores themes of school bullying, trauma, and revenge.

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_69ef840d9e3c819093615ebff4ec22be completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f63902060081909bb490327b0c16f2 completed May 2, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5a71105a948190b165e63faf23f8b9 completed July 17, 2026, 6:14 p.m.
NEDg Description generation batch_6a5a71b936e0819097fb593915d91d19 completed July 17, 2026, 6:17 p.m.
NED2 Entity disambiguation (via description) batch_6a5a720bed408190a766790b89972788 completed July 17, 2026, 6:18 p.m.
Created at: April 27, 2026, 6:08 p.m.