Triple

T6718766
Position Surface form Disambiguated ID Type / Status
Subject Nursia E153339 entity
Predicate hasDemonym P191 FINISHED
Object Nursino
Nursino is the Italian demonym for a person originating from the town of Nursia (Norcia) in Umbria, Italy.
 E614525 NE FINISHED

How this triple was built (4 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: Nursino | Statement: [Nursia, hasDemonym, Nursino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nursino
Context triple: [Nursia, hasDemonym, Nursino]
  • A. Ninji
    Ninji is a small, black, ninja-like creature from the Super Mario series known for its leaping attacks and appearances as a recurring enemy.
  • B. Babo
    Babo is a central character in Herman Melville’s novella "Benito Cereno," known as the cunning leader of a slave revolt who manipulates appearances aboard a Spanish slave ship.
  • C. Topino
    Topino is a river in central Italy that flows through the Umbria region before joining the Chiascio River.
  • D. Ramolino
    Ramolino is an Italian surname historically associated with Corsican nobility and notably borne by Letizia Ramolino, the mother of Napoleon Bonaparte.
  • E. Nobiin
    Nobiin is a Nile-Nubian language spoken primarily by Nubian communities in southern Egypt and northern Sudan, known for its ancient roots and rich oral tradition.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Nursino
Triple: [Nursia, hasDemonym, Nursino]
Generated description
Nursino is the Italian demonym for a person originating from the town of Nursia (Norcia) in Umbria, Italy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nursino
Target entity description: Nursino is the Italian demonym for a person originating from the town of Nursia (Norcia) in Umbria, Italy.
  • A. Ninji
    Ninji is a small, black, ninja-like creature from the Super Mario series known for its leaping attacks and appearances as a recurring enemy.
  • B. Babo
    Babo is a central character in Herman Melville’s novella "Benito Cereno," known as the cunning leader of a slave revolt who manipulates appearances aboard a Spanish slave ship.
  • C. Topino
    Topino is a river in central Italy that flows through the Umbria region before joining the Chiascio River.
  • D. Ramolino
    Ramolino is an Italian surname historically associated with Corsican nobility and notably borne by Letizia Ramolino, the mother of Napoleon Bonaparte.
  • E. Nobiin
    Nobiin is a Nile-Nubian language spoken primarily by Nubian communities in southern Egypt and northern Sudan, known for its ancient roots and rich oral tradition.
  • F. None of above. chosen

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_69c68809b4608190a2509ddb5ab87f05 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d135d27c819088c45839ad0e7bab completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7009b9b64819095ae1a65cd72c374 completed March 27, 2026, 10:11 p.m.
NEDg Description generation batch_69c705220cb0819081a70175c150d138 completed March 27, 2026, 10:30 p.m.
NED2 Entity disambiguation (via description) batch_69c705db3098819083ce9a93e429b758 completed March 27, 2026, 10:34 p.m.
Created at: March 27, 2026, 2:07 p.m.