Triple

T25716219
Position Surface form Disambiguated ID Type / Status
Subject Catholic Church in Uruguay E644869 entity
Predicate hasCathedral P916 FINISHED
Object Cathedral of San José de Mayo
The Cathedral of San José de Mayo is a prominent Roman Catholic church and historic landmark located in the city of San José de Mayo, Uruguay.
E1695375 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: Cathedral of San José de Mayo | Statement: [Catholic Church in Uruguay, hasCathedral, Cathedral of San José de Mayo]
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: Cathedral of San José de Mayo
Triple: [Catholic Church in Uruguay, hasCathedral, Cathedral of San José de Mayo]
Generated description
The Cathedral of San José de Mayo is a prominent Roman Catholic church and historic landmark located in the city of San José de Mayo, Uruguay.

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_69e77e8476fc8190bd5e9d05b89fad0a completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fc6211788190bb46c645902c2bc2 completed May 2, 2026, 1:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10cbf773f0819088fcbf7704dd3121 completed May 22, 2026, 9:34 p.m.
NEDg Description generation batch_6a10cd1f2f1c8190acac62d516c5d450 completed May 22, 2026, 9:39 p.m.
NED2 Entity disambiguation (via description) batch_6a10ce00cda08190a1534b9ce1cf896a completed May 22, 2026, 9:43 p.m.
Created at: April 21, 2026, 9:41 p.m.