Triple

T29092705

Position	Surface form	Disambiguated ID	Type / Status
Subject	Michel Blanc	`E734899`	entity
Predicate	notableWork	`P4`	FINISHED
Object	Kiss Whoever You Want Kiss Whoever You Want is a French ensemble romantic comedy film written and directed by Michel Blanc that interweaves the love lives and infidelities of several characters over the course of a summer vacation.	`E1851287`	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: Kiss Whoever You Want | Statement: [Michel Blanc, notableWork, Kiss Whoever You Want]

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: Kiss Whoever You Want
Triple: [Michel Blanc, notableWork, Kiss Whoever You Want]

Generated description

Kiss Whoever You Want is a French ensemble romantic comedy film written and directed by Michel Blanc that interweaves the love lives and infidelities of several characters over the course of a summer vacation.

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_69f05b0ed66481908f2e864fa550d2f1`	completed	April 28, 2026, 7 a.m.
NER	Named-entity recognition	`batch_69f6617e62fc8190b9a935835fc82bfa`	completed	May 2, 2026, 8:41 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a2537b319108190923094a66770e6f6`	completed	June 7, 2026, 9:19 a.m.
NEDg	Description generation	`batch_6a253bdcaf2c8190b24d33e76d6efc78`	completed	June 7, 2026, 9:37 a.m.
NED2	Entity disambiguation (via description)	`batch_6a253fd1f0488190abea40d50e953b04`	completed	June 7, 2026, 9:54 a.m.

Created at: April 28, 2026, 11:06 a.m.