Triple

T33452522
Position Surface form Disambiguated ID Type / Status
Subject Leopold Clement Philipp August Maria of Saxe-Coburg and Gotha E856681 entity
Predicate givenName P17 FINISHED
Object Maria
Maria is a given name used here as part of the long dynastic name of an aristocrat from the House of Saxe-Coburg and Gotha.
E2051384 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 | Statement: [Leopold Clement Philipp August Maria of Saxe-Coburg and Gotha, givenName, Maria]
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
Triple: [Leopold Clement Philipp August Maria of Saxe-Coburg and Gotha, givenName, Maria]
Generated description
Maria is a given name used here as part of the long dynastic name of an aristocrat from the House of Saxe-Coburg and Gotha.

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_69f3497281a08190b4705de0b5f26ba7 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6e4ac9b08819080f2e72323e38882 completed May 3, 2026, 6:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a358148096c8190bdbd6dbd806a207c completed June 19, 2026, 5:50 p.m.
NEDg Description generation batch_6a3583987df08190b47c4ff60f149f15 completed June 19, 2026, 5:59 p.m.
NED2 Entity disambiguation (via description) batch_6a3583ee00fc8190898daa89a0d4528c completed June 19, 2026, 6:01 p.m.
Created at: May 1, 2026, 1:37 a.m.