Triple

T30376206
Position Surface form Disambiguated ID Type / Status
Subject Bagration E772695 entity
Predicate producedNotableMember P129657 FINISHED
Object Prince Pyotr Romanovich Bagration
Prince Pyotr Romanovich Bagration was a prominent Russian nobleman and military officer from the distinguished Bagration family.
E1387166 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: Prince Pyotr Romanovich Bagration | Statement: [Bagration, producedNotableMember, Prince Pyotr Romanovich Bagration]
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: Prince Pyotr Romanovich Bagration
Triple: [Bagration, producedNotableMember, Prince Pyotr Romanovich Bagration]
Generated description
Prince Pyotr Romanovich Bagration was a prominent Russian nobleman and military officer from the distinguished Bagration family.

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_69f2248e3444819081b05712dc6873de completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69fb35c058a881909b7ffc2258a656ff completed May 6, 2026, 12:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2798a6ad3c81908a76fcbc4b928233 completed June 9, 2026, 4:37 a.m.
NEDg Description generation batch_6a279a13da1481908970a04dcb9516dc completed June 9, 2026, 4:44 a.m.
NED2 Entity disambiguation (via description) batch_6a279a938f448190b0cb68d9855c274d completed June 9, 2026, 4:46 a.m.
Created at: April 29, 2026, 8 p.m.