Triple

T36078348
Position Surface form Disambiguated ID Type / Status
Subject Ercole Ferrata E1043560 entity
Predicate notableWork P4 FINISHED
Object Reliefs in Sant'Agnese in Agone
Reliefs in Sant'Agnese in Agone are Baroque sculptural panels by Ercole Ferrata in Rome’s Sant'Agnese in Agone church, exemplifying his dramatic religious narrative style.
E2168833 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: Reliefs in Sant'Agnese in Agone | Statement: [Ercole Ferrata, notableWork, Reliefs in Sant'Agnese in Agone]
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: Reliefs in Sant'Agnese in Agone
Triple: [Ercole Ferrata, notableWork, Reliefs in Sant'Agnese in Agone]
Generated description
Reliefs in Sant'Agnese in Agone are Baroque sculptural panels by Ercole Ferrata in Rome’s Sant'Agnese in Agone church, exemplifying his dramatic religious narrative style.

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_69f76e3154908190a6f702671c2bea08 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b23b74e881909940ab743fb67a2c completed May 3, 2026, 8:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38d53e8dfc819095ae213a57f4b030 completed June 22, 2026, 6:25 a.m.
NEDg Description generation batch_6a38d5d437108190828489b1918e769f completed June 22, 2026, 6:27 a.m.
NED2 Entity disambiguation (via description) batch_6a38d69d54f48190b43baa60bfa8fe85 completed June 22, 2026, 6:30 a.m.
Created at: May 3, 2026, 4:08 p.m.