Triple

T30098740
Position Surface form Disambiguated ID Type / Status
Subject Matter of Rome E764935 entity
Predicate includesWork P2011 FINISHED
Object Roman d’Enéas
Roman d’Enéas is a 12th-century Old French verse romance that adapts Virgil’s Aeneid into the chivalric and courtly world of medieval French literature.
 E1900356 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: Roman d’Enéas | Statement: [Matter of Rome, includesWork, Roman d’Enéas]
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: Roman d’Enéas
Triple: [Matter of Rome, includesWork, Roman d’Enéas]
Generated description
Roman d’Enéas is a 12th-century Old French verse romance that adapts Virgil’s Aeneid into the chivalric and courtly world of medieval French literature.

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_69f22474e4288190b5f895fe3974aa92 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67d92dbdc8190ae3e8f67b979cb5c completed May 2, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27432cd0fc81908505e26220467f9d completed June 8, 2026, 10:33 p.m.
NEDg Description generation batch_6a274717572c819093f59219a6eb67c2 completed June 8, 2026, 10:49 p.m.
NED2 Entity disambiguation (via description) batch_6a274777b8f88190be5d159db03374a6 completed June 8, 2026, 10:51 p.m.
Created at: April 29, 2026, 7:08 p.m.