Triple

T37546459
Position Surface form Disambiguated ID Type / Status
Subject Cardinal-Deacon of San Giorgio in Velabro E933476 entity
Predicate hasTitularChurch P44808 FINISHED
Object San Giorgio in Velabro
San Giorgio in Velabro is an ancient Roman church near the Forum Boarium, notable for its early Christian origins and medieval architecture.
E2232790 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: San Giorgio in Velabro | Statement: [Cardinal-Deacon of San Giorgio in Velabro, hasTitularChurch, San Giorgio in Velabro]
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: San Giorgio in Velabro
Triple: [Cardinal-Deacon of San Giorgio in Velabro, hasTitularChurch, San Giorgio in Velabro]
Generated description
San Giorgio in Velabro is an ancient Roman church near the Forum Boarium, notable for its early Christian origins and medieval architecture.

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_69f76eca55bc8190acf25741793d5dac completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba424e66c81908d42d7bf46e6938a completed May 6, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a409f0bf9c48190804daee8af64fcf5 completed June 28, 2026, 4:11 a.m.
NEDg Description generation batch_6a40a0bb718081909f6f7d021b52070c completed June 28, 2026, 4:19 a.m.
NED2 Entity disambiguation (via description) batch_6a40a180222c8190aa3f63942e798f2f completed June 28, 2026, 4:22 a.m.
Created at: May 3, 2026, 4:17 p.m.