Triple

T30836946
Position Surface form Disambiguated ID Type / Status
Subject Bourbon-Parma family E785388 entity
Predicate hasMember P10 FINISHED
Object Maria Cristina of Bourbon-Parma
Maria Cristina of Bourbon-Parma was a princess of the Bourbon-Parma branch of the Bourbon dynasty, connected to various European royal families through birth and marriage.
E1974889 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: Maria Cristina of Bourbon-Parma | Statement: [Bourbon-Parma family, hasMember, Maria Cristina of Bourbon-Parma]
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: Maria Cristina of Bourbon-Parma
Triple: [Bourbon-Parma family, hasMember, Maria Cristina of Bourbon-Parma]
Generated description
Maria Cristina of Bourbon-Parma was a princess of the Bourbon-Parma branch of the Bourbon dynasty, connected to various European royal families through birth and marriage.

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_69f224b73d8c81908129383bfb397c87 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6913f9c4c8190b5984101070d067c completed May 3, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b848ded3481908d1e3d70b0167d3e completed June 12, 2026, 4:01 a.m.
NEDg Description generation batch_6a2b8de5584c8190afea9b72664e75e5 completed June 12, 2026, 4:41 a.m.
NED2 Entity disambiguation (via description) batch_6a2b8f7201dc8190aca36312566296ad completed June 12, 2026, 4:47 a.m.
Created at: April 29, 2026, 8:45 p.m.