Triple

T25042953
Position Surface form Disambiguated ID Type / Status
Subject Les Belles de nuit E627159 entity
Predicate alsoKnownAs P39 FINISHED
Object Beauties of the Night
Beauties of the Night is a 1952 French fantasy-comedy film directed by René Clair that follows a young composer whose romantic dreams begin to intrude on his everyday life.
 E1661122 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: Beauties of the Night | Statement: [Les Belles de nuit, alsoKnownAs, Beauties of the Night]
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: Beauties of the Night
Triple: [Les Belles de nuit, alsoKnownAs, Beauties of the Night]
Generated description
Beauties of the Night is a 1952 French fantasy-comedy film directed by René Clair that follows a young composer whose romantic dreams begin to intrude on his everyday life.

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_69e2ff2b4c80819087c916b2b16241b9 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4530d80148190b959bb48ff7e0c2f completed May 1, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048c44c748190ab184ba085a0d92b completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a1049b747c48190a0b61cbd96172411 completed May 22, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_6a104a7bba188190b4d819ed6c618086 completed May 22, 2026, 12:22 p.m.
Created at: April 18, 2026, 6:08 a.m.