Triple

T26392571
Position Surface form Disambiguated ID Type / Status
Subject Shawn Dou E663457 entity
Predicate notableWork P4 FINISHED
Object Ten Years Late
Ten Years Late is a Chinese romantic drama television series starring Shawn Dou, adapted from a popular Japanese manga about love, career, and reunion after years of separation.
 E1720853 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: Ten Years Late | Statement: [Shawn Dou, notableWork, Ten Years Late]
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: Ten Years Late
Triple: [Shawn Dou, notableWork, Ten Years Late]
Generated description
Ten Years Late is a Chinese romantic drama television series starring Shawn Dou, adapted from a popular Japanese manga about love, career, and reunion after years of separation.

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_69ee883823988190b418b111be28a44a completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610c024f081908237794984538566 completed May 2, 2026, 2:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a119a830a60819086bfd7fafd437a9b completed May 23, 2026, 12:16 p.m.
NEDg Description generation batch_6a119b15cbb4819087ea26f6c87d8732 completed May 23, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a119bad614481909156c765ce266350 completed May 23, 2026, 12:21 p.m.
Created at: April 26, 2026, 11:26 p.m.