Triple

T30742138
Position Surface form Disambiguated ID Type / Status
Subject Li Chu E782719 entity
Predicate mother P120 FINISHED
Object Empress Zhang of Tang
Empress Zhang of Tang was a consort of the Tang dynasty who became empress as the wife of Emperor Suzong and mother of his successor, Emperor Daizong (Li Chu).
 E2167686 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: Empress Zhang of Tang | Statement: [Li Chu, mother, Empress Zhang of Tang]
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: Empress Zhang of Tang
Triple: [Li Chu, mother, Empress Zhang of Tang]
Generated description
Empress Zhang of Tang was a consort of the Tang dynasty who became empress as the wife of Emperor Suzong and mother of his successor, Emperor Daizong (Li Chu).

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_69f224aeb1588190897d395e8ed2acb8 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68f6a32f081909499d6ca20f35e55 completed May 2, 2026, 11:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38d5171bec8190bf434fbb9f9ad83c completed June 22, 2026, 6:24 a.m.
NEDg Description generation batch_6a38d60d968081908071371e5bbc1314 completed June 22, 2026, 6:28 a.m.
NED2 Entity disambiguation (via description) batch_6a38d6b7722c81909093057618f2569a completed June 22, 2026, 6:31 a.m.
Created at: April 29, 2026, 8:38 p.m.