Triple

T35094680

Position	Surface form	Disambiguated ID	Type / Status
Subject	Le Cabinet des Antiques	`E1012834`	entity
Predicate	hasCharacter	`P2308`	FINISHED
Object	Marquis d’Esgrignon Marquis d’Esgrignon is an aristocratic character in Honoré de Balzac’s Comédie Humaine, emblematic of the declining French nobility in the post-Revolutionary era.	`E2141587`	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: Marquis d’Esgrignon | Statement: [Le Cabinet des Antiques, hasCharacter, Marquis d’Esgrignon]

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: Marquis d’Esgrignon
Triple: [Le Cabinet des Antiques, hasCharacter, Marquis d’Esgrignon]

Generated description

Marquis d’Esgrignon is an aristocratic character in Honoré de Balzac’s Comédie Humaine, emblematic of the declining French nobility in the post-Revolutionary era.

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_69f76dd432ec8190969bc32acfc152b1`	completed	May 3, 2026, 3:46 p.m.
NER	Named-entity recognition	`batch_69f78be337b88190ab6ecbc97a7517f6`	completed	May 3, 2026, 5:54 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a384017c71481908444d52ef44b1e15`	completed	June 21, 2026, 7:48 p.m.
NEDg	Description generation	`batch_6a3840adcad081908294292104447b0c`	completed	June 21, 2026, 7:51 p.m.
NED2	Entity disambiguation (via description)	`batch_6a38411c749881908ea838276aeed039`	completed	June 21, 2026, 7:53 p.m.

Created at: May 3, 2026, 4:01 p.m.