Triple

T37175952
Position Surface form Disambiguated ID Type / Status
Subject Borujerd E921044 entity
Predicate hasReligiousSite P916 FINISHED
Object Jameh Mosque of Borujerd
The Jameh Mosque of Borujerd is a historic congregational mosque in western Iran, renowned for its centuries-old Islamic architecture and cultural significance.
E2216490 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: Jameh Mosque of Borujerd | Statement: [Borujerd, hasReligiousSite, Jameh Mosque of Borujerd]
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: Jameh Mosque of Borujerd
Triple: [Borujerd, hasReligiousSite, Jameh Mosque of Borujerd]
Generated description
The Jameh Mosque of Borujerd is a historic congregational mosque in western Iran, renowned for its centuries-old Islamic architecture and cultural 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_69f76ea16f288190b445aa1604d996f4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb35ee03088190aae12ccc43fadcc1 completed May 6, 2026, 12:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a402bc329f8819096dcef18f40e0dd5 completed June 27, 2026, 8 p.m.
NEDg Description generation batch_6a402daca388819092fcfc9ca6316db3 completed June 27, 2026, 8:08 p.m.
NED2 Entity disambiguation (via description) batch_6a402fb5734c8190864a6094af82a89b completed June 27, 2026, 8:16 p.m.
Created at: May 3, 2026, 4:15 p.m.