Triple

T26887765
Position Surface form Disambiguated ID Type / Status
Subject Città Sant’Angelo E677088 entity
Predicate hasReligiousBuilding P1191 FINISHED
Object Church of Santa Chiara
The Church of Santa Chiara is a historic Catholic church in the Italian town of Città Sant’Angelo, noted for its religious and architectural significance.
E1750439 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: Church of Santa Chiara | Statement: [Città Sant’Angelo, hasReligiousBuilding, Church of Santa Chiara]
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: Church of Santa Chiara
Triple: [Città Sant’Angelo, hasReligiousBuilding, Church of Santa Chiara]
Generated description
The Church of Santa Chiara is a historic Catholic church in the Italian town of Città Sant’Angelo, noted for its religious and architectural significance.

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_69eee9bc0c90819085608c8bdc513a57 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61f65926c8190a7028986658e966e completed May 2, 2026, 3:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12298c5e108190afc4ecf55db87fee completed May 23, 2026, 10:26 p.m.
NEDg Description generation batch_6a122a8570488190a59ab7f4422cc63d completed May 23, 2026, 10:30 p.m.
NED2 Entity disambiguation (via description) batch_6a122af21ba88190b6779cd1c12861a1 completed May 23, 2026, 10:32 p.m.
Created at: April 27, 2026, 5:43 a.m.