Triple

T32624302
Position Surface form Disambiguated ID Type / Status
Subject Emperor Zhaozong of Tang E834011 entity
Predicate child P120 FINISHED
Object Li Zuoji
Li Zuoji was a lesser-known prince of the late Tang dynasty, recognized primarily as one of the sons of Emperor Zhaozong of Tang.
E2143712 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: Li Zuoji | Statement: [Emperor Zhaozong of Tang, child, Li Zuoji]
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: Li Zuoji
Triple: [Emperor Zhaozong of Tang, child, Li Zuoji]
Generated description
Li Zuoji was a lesser-known prince of the late Tang dynasty, recognized primarily as one of the sons of Emperor Zhaozong of Tang.

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_69f3492ccc80819086ef7d26e9786647 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c6f1aed881908065e90d2f44a399 completed May 3, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a384a11f5f08190b635329a29b1284b completed June 21, 2026, 8:31 p.m.
NEDg Description generation batch_6a384ad781b48190b37e3ae4708eae57 completed June 21, 2026, 8:34 p.m.
NED2 Entity disambiguation (via description) batch_6a384b6644208190b1c18024a063846b completed June 21, 2026, 8:36 p.m.
Created at: May 1, 2026, 1:06 a.m.