Triple

T28590352
Position Surface form Disambiguated ID Type / Status
Subject Yelü clan E723621 entity
Predicate notableMember P10 FINISHED
Object Yelü Chucai
Yelü Chucai was a prominent Khitan statesman and Confucian scholar who served as chief adviser to Genghis Khan and Ögedei Khan, helping to shape the early administration of the Mongol Empire.
E1879639 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: Yelü Chucai | Statement: [Yelü clan, notableMember, Yelü Chucai]
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: Yelü Chucai
Triple: [Yelü clan, notableMember, Yelü Chucai]
Generated description
Yelü Chucai was a prominent Khitan statesman and Confucian scholar who served as chief adviser to Genghis Khan and Ögedei Khan, helping to shape the early administration of the Mongol Empire.

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_69f01d7f92e481909847f5f3f3174a89 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f651b2a2388190b269d7b5e931794a completed May 2, 2026, 7:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a267e8d6ed48190bd48b474c1ad2ec4 completed June 8, 2026, 8:34 a.m.
NEDg Description generation batch_6a2682d3fa3c81909e0736cb74338f7e completed June 8, 2026, 8:52 a.m.
NED2 Entity disambiguation (via description) batch_6a26883b773081908ee6cad8a66f0251 completed June 8, 2026, 9:15 a.m.
Created at: April 28, 2026, 4:20 a.m.