Triple

T23654915
Position Surface form Disambiguated ID Type / Status
Subject Urbino Cathedral E584269 entity
Predicate containsWorkOfArtBy P5419 FINISHED
Object Giovanni Francesco Guerrieri
Giovanni Francesco Guerrieri was an Italian Baroque painter known for his religious works and dramatic use of light and shadow, active mainly in the Marche region.
E1737957 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: Giovanni Francesco Guerrieri | Statement: [Urbino Cathedral, containsWorkOfArtBy, Giovanni Francesco Guerrieri]
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: Giovanni Francesco Guerrieri
Triple: [Urbino Cathedral, containsWorkOfArtBy, Giovanni Francesco Guerrieri]
Generated description
Giovanni Francesco Guerrieri was an Italian Baroque painter known for his religious works and dramatic use of light and shadow, active mainly in the Marche region.

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_69f1b35a513c8190a251fe2f55b23d7c completed April 29, 2026, 7:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe39a89c81909f3a19e02ddb72ed completed May 23, 2026, 7:21 p.m.
NEDg Description generation batch_6a11ff4907e88190aaad22b7390bc094 completed May 23, 2026, 7:26 p.m.
NED2 Entity disambiguation (via description) batch_6a1200461b94819098a2cbd8b03d4076 completed May 23, 2026, 7:30 p.m.
Created at: April 17, 2026, 6:49 p.m.