Triple

T22145472
Position Surface form Disambiguated ID Type / Status
Subject Charles Robert of Hungary E547275 entity
Predicate spouse P13 FINISHED
Object Beatrice of Luxembourg
Beatrice of Luxembourg was a 14th-century Luxembourgish princess who became Queen consort of Hungary through her marriage to King Charles I (Charles Robert) of Hungary.
E1673000 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: Beatrice of Luxembourg | Statement: [Charles Robert of Hungary, spouse, Beatrice of Luxembourg]
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: Beatrice of Luxembourg
Triple: [Charles Robert of Hungary, spouse, Beatrice of Luxembourg]
Generated description
Beatrice of Luxembourg was a 14th-century Luxembourgish princess who became Queen consort of Hungary through her marriage to King Charles I (Charles Robert) of Hungary.

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_69e11e3a95d88190a3bd80d9471976c3 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f129efd52c8190ab0acff5bbfc0d77 completed April 28, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10678a76b08190a10997ab390d5cb3 completed May 22, 2026, 2:26 p.m.
NEDg Description generation batch_6a1069eb58c0819082da82491147d2ba completed May 22, 2026, 2:36 p.m.
NED2 Entity disambiguation (via description) batch_6a106a7f1248819084a440a1d4bf20c0 completed May 22, 2026, 2:38 p.m.
Created at: April 16, 2026, 8:33 p.m.