Triple

T26237021
Position Surface form Disambiguated ID Type / Status
Subject Catholic Church in Portugal E656200 entity
Predicate hasDiocese P2740 FINISHED
Object Diocese of Évora
The Diocese of Évora is a historic Roman Catholic ecclesiastical territory in southern Portugal centered on the city of Évora and overseen by a diocesan bishop.
E1725375 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: Diocese of Évora | Statement: [Catholic Church in Portugal, hasDiocese, Diocese of Évora]
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: Diocese of Évora
Triple: [Catholic Church in Portugal, hasDiocese, Diocese of Évora]
Generated description
The Diocese of Évora is a historic Roman Catholic ecclesiastical territory in southern Portugal centered on the city of Évora and overseen by a diocesan bishop.

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_69ee5b4b8b408190993da38c0067cc8d completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60d8b784c819088ad58083c2e27b5 completed May 2, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11aea0f5b0819089a39b705d00abda completed May 23, 2026, 1:41 p.m.
NEDg Description generation batch_6a11b01df9348190a991161aa1fb8018 completed May 23, 2026, 1:48 p.m.
NED2 Entity disambiguation (via description) batch_6a11b1230c148190931c49c9df40caa4 completed May 23, 2026, 1:52 p.m.
Created at: April 26, 2026, 9:02 p.m.