Triple

T24111989
Position Surface form Disambiguated ID Type / Status
Subject Duchy of Naples E597400 entity
Predicate notableRuler P22 FINISHED
Object John I of Naples
John I of Naples was a 9th-century duke who ruled the Duchy of Naples and played a key role in its political and military affairs during a period of shifting alliances in southern Italy.
E1629271 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: John I of Naples | Statement: [Duchy of Naples, notableRuler, John I of Naples]
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: John I of Naples
Triple: [Duchy of Naples, notableRuler, John I of Naples]
Generated description
John I of Naples was a 9th-century duke who ruled the Duchy of Naples and played a key role in its political and military affairs during a period of shifting alliances in southern Italy.

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_69e288c60f9c8190af948d7354aedbeb completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1de1bd81c8190a44f07487d2ba176 completed April 29, 2026, 10:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9a04ca081908e740222b18fb506 completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fcdabbd488190b5fd6ea22494e941 completed May 22, 2026, 3:29 a.m.
NED2 Entity disambiguation (via description) batch_6a0fce302f6081909a462e08d08c5bb7 completed May 22, 2026, 3:32 a.m.
Created at: April 17, 2026, 11:03 p.m.