Triple

T37164969
Position Surface form Disambiguated ID Type / Status
Subject Revúca E920768 entity
Predicate hasLandmark P105 FINISHED
Object Roman Catholic Church of St. Vavrinec
The Roman Catholic Church of St. Vavrinec is a historic parish church and notable religious landmark in the town of Revúca, Slovakia.
 E2216129 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: Roman Catholic Church of St. Vavrinec | Statement: [Revúca, hasLandmark, Roman Catholic Church of St. Vavrinec]
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: Roman Catholic Church of St. Vavrinec
Triple: [Revúca, hasLandmark, Roman Catholic Church of St. Vavrinec]
Generated description
The Roman Catholic Church of St. Vavrinec is a historic parish church and notable religious landmark in the town of Revúca, Slovakia.

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_69f76ea0429081908c711b55599eac3c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb35c56b708190a094ce286a1e99b9 completed May 6, 2026, 12:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a402bbc0db88190b2f1417698f3785d completed June 27, 2026, 7:59 p.m.
NEDg Description generation batch_6a402c23c4e481908e808481ba088b5f completed June 27, 2026, 8:01 p.m.
NED2 Entity disambiguation (via description) batch_6a40304ade9881909cdbe86d75532576 completed June 27, 2026, 8:19 p.m.
Created at: May 3, 2026, 4:15 p.m.