Triple

T27485672
Position Surface form Disambiguated ID Type / Status
Subject Ferenc Gyurcsány E693728 entity
Predicate employer P7 FINISHED
Object Altus Befektetési és Vagyonkezelő Rt.
Altus Befektetési és Vagyonkezelő Rt. is a Hungarian investment and asset management company best known as the business vehicle through which Ferenc Gyurcsány built much of his pre-political fortune.
E1776452 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: Altus Befektetési és Vagyonkezelő Rt. | Statement: [Ferenc Gyurcsány, employer, Altus Befektetési és Vagyonkezelő Rt.]
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: Altus Befektetési és Vagyonkezelő Rt.
Triple: [Ferenc Gyurcsány, employer, Altus Befektetési és Vagyonkezelő Rt.]
Generated description
Altus Befektetési és Vagyonkezelő Rt. is a Hungarian investment and asset management company best known as the business vehicle through which Ferenc Gyurcsány built much of his pre-political fortune.

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_69ef5382b9648190be0b1ef2ad5d043c completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e848b008190bd7314c9a0f884a3 completed May 2, 2026, 5:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbeea53081909e5bb5854989d522 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12be271e6c819092522780e794946d completed May 24, 2026, 9 a.m.
NED2 Entity disambiguation (via description) batch_6a12be8658608190ba55a4cb196a39ce completed May 24, 2026, 9:01 a.m.
Created at: April 27, 2026, 1:02 p.m.