Triple

T25371146

Position	Surface form	Disambiguated ID	Type / Status
Subject	Count of Nola	`E632933`	entity
Predicate	hasAlternativeLabel	`P39`	FINISHED
Object	Comes Nolanus Comes Nolanus is an alternative Latin title referring to the medieval noble known as the Count of Nola, a feudal ruler associated with the town of Nola in southern Italy.	`E1676854`	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: Comes Nolanus | Statement: [Count of Nola, hasAlternativeLabel, Comes Nolanus]

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: Comes Nolanus
Triple: [Count of Nola, hasAlternativeLabel, Comes Nolanus]

Generated description

Comes Nolanus is an alternative Latin title referring to the medieval noble known as the Count of Nola, a feudal ruler associated with the town of Nola 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_69e75a90c0dc819092f928b6ea0ecc72`	completed	April 21, 2026, 11:08 a.m.
NER	Named-entity recognition	`batch_69f4a11323e08190a8ec4687f8fefe7f`	completed	May 1, 2026, 12:48 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a107607ebd88190b55693402f06c47f`	completed	May 22, 2026, 3:28 p.m.
NEDg	Description generation	`batch_6a10775af30c8190b81d59d29bf57a2e`	completed	May 22, 2026, 3:33 p.m.
NED2	Entity disambiguation (via description)	`batch_6a1078eaf8888190b3453537d13d6cc5`	completed	May 22, 2026, 3:40 p.m.

Created at: April 21, 2026, 1:38 p.m.