Triple

T27992783
Position Surface form Disambiguated ID Type / Status
Subject Xu E706924 entity
Predicate hasNotableBearer P458 FINISHED
Object Xu Da
Xu Da was a prominent 14th-century Chinese general who helped overthrow the Yuan dynasty and establish the Ming dynasty under the Hongwu Emperor.
 E1798551 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: Xu Da | Statement: [Xu, hasNotableBearer, Xu Da]
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: Xu Da
Triple: [Xu, hasNotableBearer, Xu Da]
Generated description
Xu Da was a prominent 14th-century Chinese general who helped overthrow the Yuan dynasty and establish the Ming dynasty under the Hongwu Emperor.

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_69ef96b980d88190a753b2f9a978595a completed April 27, 2026, 5:02 p.m.
NER Named-entity recognition batch_69f63ba8ff308190876c52b659e5979d completed May 2, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15b894bd288190a6a4f6d20075dc15 completed May 26, 2026, 3:13 p.m.
NEDg Description generation batch_6a15b947032c8190a37847bfe71c1d54 completed May 26, 2026, 3:16 p.m.
NED2 Entity disambiguation (via description) batch_6a15ba581c108190b6fc6480bf71fcb0 completed May 26, 2026, 3:20 p.m.
Created at: April 27, 2026, 7:51 p.m.