Triple

T23658197
Position Surface form Disambiguated ID Type / Status
Subject King Vukašin Mrnjavčević E584366 entity
Predicate spouse P13 FINISHED
Object Alena (Helena) Mrnjavčević
Alena (Helena) Mrnjavčević was a 14th-century Serbian noblewoman best known as the wife of King Vukašin Mrnjavčević of the Mrnjavčević dynasty.
E1602311 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: Alena (Helena) Mrnjavčević | Statement: [King Vukašin Mrnjavčević, spouse, Alena (Helena) Mrnjavčević]
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: Alena (Helena) Mrnjavčević
Triple: [King Vukašin Mrnjavčević, spouse, Alena (Helena) Mrnjavčević]
Generated description
Alena (Helena) Mrnjavčević was a 14th-century Serbian noblewoman best known as the wife of King Vukašin Mrnjavčević of the Mrnjavčević dynasty.

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_69e248ffc0888190ae23c4731eb8b7ac completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b35ce4bc81909a26bc7e44a929d8 completed April 29, 2026, 7:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f53a402a88190b6ccc7e7fb4b45bf completed May 21, 2026, 6:49 p.m.
NEDg Description generation batch_6a0f58f239ac8190ab0cc8cd7272a208 completed May 21, 2026, 7:11 p.m.
NED2 Entity disambiguation (via description) batch_6a0f59dfebe0819095c934359cb5dc24 completed May 21, 2026, 7:15 p.m.
Created at: April 17, 2026, 6:49 p.m.