Triple

T29473876
Position Surface form Disambiguated ID Type / Status
Subject Natacha von Braun E747587 entity
Predicate givenName P17 FINISHED
Object Natacha
Natacha is a feminine given name of Slavic origin, commonly used in French- and Russian-speaking cultures as a variant of Natasha.
E1868833 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: Natacha | Statement: [Natacha von Braun, givenName, Natacha]
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: Natacha
Triple: [Natacha von Braun, givenName, Natacha]
Generated description
Natacha is a feminine given name of Slavic origin, commonly used in French- and Russian-speaking cultures as a variant of Natasha.

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_69f0bd42cf308190bb01b20bc5b7c2d0 completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66bd3176c8190a23f2ab61210bd2f completed May 2, 2026, 9:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25f11ca72881909056a3d96d53fec1 completed June 7, 2026, 10:30 p.m.
NEDg Description generation batch_6a25f5cdbc34819084ef618b265d88f4 completed June 7, 2026, 10:50 p.m.
NED2 Entity disambiguation (via description) batch_6a25f994332c8190a75fa8a2e64bc7b1 completed June 7, 2026, 11:07 p.m.
Created at: April 28, 2026, 3:59 p.m.