Triple

T25611836

Position	Surface form	Disambiguated ID	Type / Status
Subject	L’invention du quotidien	`E642058`	entity
Predicate	publisher	`P29`	FINISHED
Object	Union générale d’éditions Union générale d’éditions was a French publishing house known for its influential intellectual and critical works, particularly in the humanities and social sciences.	`E1686508`	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: Union générale d’éditions | Statement: [L’invention du quotidien, publisher, Union générale d’éditions]

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: Union générale d’éditions
Triple: [L’invention du quotidien, publisher, Union générale d’éditions]

Generated description

Union générale d’éditions was a French publishing house known for its influential intellectual and critical works, particularly in the humanities and social sciences.

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_69e75dc6ccf081908d49578fd36a76d5`	completed	April 21, 2026, 11:21 a.m.
NER	Named-entity recognition	`batch_69f5f9e386c08190a55d0b3147fb74c1`	completed	May 2, 2026, 1:19 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10b7777d9c81909e33edce7e132cea`	completed	May 22, 2026, 8:07 p.m.
NEDg	Description generation	`batch_6a10b84ad3ac8190b78bec4cd68a84e8`	completed	May 22, 2026, 8:10 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10b97dedd48190858687f050f15f7b`	completed	May 22, 2026, 8:15 p.m.

Created at: April 21, 2026, 4:42 p.m.