Triple

T30836945
Position Surface form Disambiguated ID Type / Status
Subject Bourbon-Parma family E785388 entity
Predicate hasMember P10 FINISHED
Object Maria Anna of Bourbon-Parma
Maria Anna of Bourbon-Parma was a princess of the Bourbon-Parma branch of the Spanish royal House of Bourbon, known for her dynastic ties to several European royal families.
E1966494 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 Anna of Bourbon-Parma | Statement: [Bourbon-Parma family, hasMember, Maria Anna 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 Anna of Bourbon-Parma
Triple: [Bourbon-Parma family, hasMember, Maria Anna of Bourbon-Parma]
Generated description
Maria Anna of Bourbon-Parma was a princess of the Bourbon-Parma branch of the Spanish royal House of Bourbon, known for her dynastic ties to several European royal families.

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_6a2b2d5cfd4c819080bcf8fce6471313 completed June 11, 2026, 9:49 p.m.
NEDg Description generation batch_6a2b2ddd57c48190a1a84971148c9a8a completed June 11, 2026, 9:51 p.m.
NED2 Entity disambiguation (via description) batch_6a2b2e4cb6808190b20640e6279bd56a completed June 11, 2026, 9:53 p.m.
Created at: April 29, 2026, 8:45 p.m.