Triple

T34749966
Position Surface form Disambiguated ID Type / Status
Subject Mezőkövesd E1001742 entity
Predicate hasMinority P4710 FINISHED
Object Matyó people
The Matyó people are a Hungarian ethnic group renowned for their richly colored folk costumes and intricate floral embroidery traditions, particularly associated with the town of Mezőkövesd.
E2296193 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: Matyó people | Statement: [Mezőkövesd, hasMinority, Matyó people]
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: Matyó people
Triple: [Mezőkövesd, hasMinority, Matyó people]
Generated description
The Matyó people are a Hungarian ethnic group renowned for their richly colored folk costumes and intricate floral embroidery traditions, particularly associated with the town of Mezőkövesd.

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_69f76db0367081909b57c50a7fb03025 completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f779eab83481909e041bdfbebff34c completed May 3, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a8246c36ed48190a0e3a9865ef684dc completed Aug. 16, 2026, 11:24 p.m.
NEDg Description generation batch_6a8248ebc1648190a3da3f0395f79d27 completed Aug. 16, 2026, 11:34 p.m.
NED2 Entity disambiguation (via description) batch_6a824910fe208190b286c40ee96623e6 completed Aug. 16, 2026, 11:34 p.m.
Created at: May 3, 2026, 3:59 p.m.