Triple

T28757477
Position Surface form Disambiguated ID Type / Status
Subject Emperor Shengzong of Liao E731709 entity
Predicate predecessor P97 FINISHED
Object Emperor Jingzong of Liao
Emperor Jingzong of Liao was an early Khitan ruler of the Liao dynasty who helped consolidate imperial authority and shape the dynasty’s emerging political institutions.
E1835090 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: Emperor Jingzong of Liao | Statement: [Emperor Shengzong of Liao, predecessor, Emperor Jingzong of Liao]
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: Emperor Jingzong of Liao
Triple: [Emperor Shengzong of Liao, predecessor, Emperor Jingzong of Liao]
Generated description
Emperor Jingzong of Liao was an early Khitan ruler of the Liao dynasty who helped consolidate imperial authority and shape the dynasty’s emerging political institutions.

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_69f043ed68a881909e858a06bab7a247 completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f657fc362881909ae596789cf9c797 completed May 2, 2026, 8:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26613e946c81909fc0347f8d8e8987 completed June 8, 2026, 6:29 a.m.
NEDg Description generation batch_6a26658b86e88190b68b3a7d183a72e9 completed June 8, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_6a266c326a7081909d55ff20b5c3b851 completed June 8, 2026, 7:16 a.m.
Created at: April 28, 2026, 6:10 a.m.