Triple

T36066607
Position Surface form Disambiguated ID Type / Status
Subject Chiesa di Santa Maria in Monticelli E1043247 entity
Predicate hasAlternativeName P39 FINISHED
Object Santa Maria in Monticelli
Santa Maria in Monticelli is a historic Roman Catholic church in Rome, Italy, known for its medieval origins and subsequent Baroque renovations.
E2168255 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: Santa Maria in Monticelli | Statement: [Chiesa di Santa Maria in Monticelli, hasAlternativeName, Santa Maria in Monticelli]
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: Santa Maria in Monticelli
Triple: [Chiesa di Santa Maria in Monticelli, hasAlternativeName, Santa Maria in Monticelli]
Generated description
Santa Maria in Monticelli is a historic Roman Catholic church in Rome, Italy, known for its medieval origins and subsequent Baroque renovations.

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_69f76e2fd3248190b900d9a492bf5a7a completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b21808f881908de13f0442f580ef completed May 3, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38d536f78c8190ac037c559130d678 completed June 22, 2026, 6:24 a.m.
NEDg Description generation batch_6a38d5d386b08190a918dfb8dc7d18e5 completed June 22, 2026, 6:27 a.m.
NED2 Entity disambiguation (via description) batch_6a38d681cf388190896a30e2b0939181 completed June 22, 2026, 6:30 a.m.
Created at: May 3, 2026, 4:08 p.m.