Triple

T24820971
Position Surface form Disambiguated ID Type / Status
Subject Façade of San Marcello al Corso, Rome E621062 entity
Predicate partOf P40 FINISHED
Object San Marcello al Corso church
San Marcello al Corso church is a historic Roman Catholic church in central Rome, renowned for its Baroque façade and rich artistic and religious heritage.
E1655473 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 Marcello al Corso church | Statement: [Façade of San Marcello al Corso, Rome, partOf, San Marcello al Corso church]
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 Marcello al Corso church
Triple: [Façade of San Marcello al Corso, Rome, partOf, San Marcello al Corso church]
Generated description
San Marcello al Corso church is a historic Roman Catholic church in central Rome, renowned for its Baroque façade and rich artistic and religious heritage.

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_69e2fabfd4648190bd0e5c7f4dbb6cab completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f42297fe00819087f0b7d666b7938a completed May 1, 2026, 3:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1033118da4819094755a865c84d47e completed May 22, 2026, 10:42 a.m.
NEDg Description generation batch_6a1033be69f88190988f54e89df5438a completed May 22, 2026, 10:45 a.m.
NED2 Entity disambiguation (via description) batch_6a10346cdcac8190865eb3c1b86c9c2c completed May 22, 2026, 10:48 a.m.
Created at: April 18, 2026, 5:04 a.m.