Triple

T28794384
Position Surface form Disambiguated ID Type / Status
Subject Catholic Church in Vietnam E727045 entity
Predicate hasPart P35 FINISHED
Object Diocese of Nha Trang
The Diocese of Nha Trang is a territorial jurisdiction of the Roman Catholic Church located in the coastal city of Nha Trang in central Vietnam.
E1844909 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 Nha Trang | Statement: [Catholic Church in Vietnam, hasPart, Diocese of Nha Trang]
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 Nha Trang
Triple: [Catholic Church in Vietnam, hasPart, Diocese of Nha Trang]
Generated description
The Diocese of Nha Trang is a territorial jurisdiction of the Roman Catholic Church located in the coastal city of Nha Trang in central Vietnam.

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_69f0319b7c44819085736bcc256185e6 completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f6587c26308190a6dce7e40a1eec82 completed May 2, 2026, 8:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a250595e39881908eb1e6ac056d1013 completed June 7, 2026, 5:45 a.m.
NEDg Description generation batch_6a2509f0d7048190b5cc1971e6503653 completed June 7, 2026, 6:04 a.m.
NED2 Entity disambiguation (via description) batch_6a250e35cb2c81909d7632be22680434 completed June 7, 2026, 6:22 a.m.
Created at: April 28, 2026, 6:24 a.m.