Triple

T22003916
Position Surface form Disambiguated ID Type / Status
Subject Along with the Gods: The Two Worlds E543401 entity
Predicate producer P490 FINISHED
Object Won Dong-yeon
Won Dong-yeon is a South Korean film producer known for his work on major commercial hits, including the fantasy blockbuster "Along with the Gods: The Two Worlds."
E1873743 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: Won Dong-yeon | Statement: [Along with the Gods: The Two Worlds, producer, Won Dong-yeon]
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: Won Dong-yeon
Triple: [Along with the Gods: The Two Worlds, producer, Won Dong-yeon]
Generated description
Won Dong-yeon is a South Korean film producer known for his work on major commercial hits, including the fantasy blockbuster "Along with the Gods: The Two Worlds."

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_69e11e2c814c8190837d072789000486 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1276cab5c8190ac1236fde7e0394a completed April 28, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a262d3797d88190957a5160094aa576 completed June 8, 2026, 2:47 a.m.
NEDg Description generation batch_6a2631635b348190a628533ebaab1a6b completed June 8, 2026, 3:05 a.m.
NED2 Entity disambiguation (via description) batch_6a26358d611c8190904db2b471839ee3 completed June 8, 2026, 3:22 a.m.
Created at: April 16, 2026, 8:20 p.m.