Triple

T27475937
Position Surface form Disambiguated ID Type / Status
Subject Pesaro Cathedral E693457 entity
Predicate nativeName P15 FINISHED
Object Cattedrale di San Terenzio
Cattedrale di San Terenzio is the Italian Roman Catholic cathedral in Pesaro, Marche, notable for its layered architectural history from Romanesque foundations to later Gothic and Baroque additions.
 E1775629 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: Cattedrale di San Terenzio | Statement: [Pesaro Cathedral, nativeName, Cattedrale di San Terenzio]
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: Cattedrale di San Terenzio
Triple: [Pesaro Cathedral, nativeName, Cattedrale di San Terenzio]
Generated description
Cattedrale di San Terenzio is the Italian Roman Catholic cathedral in Pesaro, Marche, notable for its layered architectural history from Romanesque foundations to later Gothic and Baroque additions.

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_69ef5381f2648190a2392d0fab833095 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e43e958819087804afccef56697 completed May 2, 2026, 5:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbe717ec8190bc12eee81e3851f2 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bce144b481909ef46950ddf8236a completed May 24, 2026, 8:54 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd75c610819081ae1b4f7fedb4cb completed May 24, 2026, 8:57 a.m.
Created at: April 27, 2026, 12:57 p.m.