Triple

T25717871

Position	Surface form	Disambiguated ID	Type / Status
Subject	French chambers of commerce and industry network	`E644907`	entity
Predicate	relatedTo	`P37`	FINISHED
Object	CCI France CCI France is the national body that coordinates and represents the network of French chambers of commerce and industry in support of businesses and economic development.	`E1690471`	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: CCI France | Statement: [French chambers of commerce and industry network, relatedTo, CCI France]

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: CCI France
Triple: [French chambers of commerce and industry network, relatedTo, CCI France]

Generated description

CCI France is the national body that coordinates and represents the network of French chambers of commerce and industry in support of businesses and economic development.

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_69e77e8476fc8190bd5e9d05b89fad0a`	completed	April 21, 2026, 1:41 p.m.
NER	Named-entity recognition	`batch_69f5fc64597c8190bfd867fb93834195`	completed	May 2, 2026, 1:30 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10c178e2888190a64beebb575813d9`	completed	May 22, 2026, 8:50 p.m.
NEDg	Description generation	`batch_6a10c2d613848190a1cf2fbcaca2a1c4`	completed	May 22, 2026, 8:55 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10c3428a0481909a49ed3600c675aa`	completed	May 22, 2026, 8:57 p.m.

Created at: April 21, 2026, 9:48 p.m.