Triple

T24518489
Position Surface form Disambiguated ID Type / Status
Subject Lux Film E606447 entity
Predicate hasAlternativeName P39 FINISHED
Object Lux Film S.p.A.
Lux Film S.p.A. was an Italian film production and distribution company active mainly in the mid-20th century, known for producing notable Italian and European cinema.
 E1638838 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: Lux Film S.p.A. | Statement: [Lux Film, hasAlternativeName, Lux Film S.p.A.]
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: Lux Film S.p.A.
Triple: [Lux Film, hasAlternativeName, Lux Film S.p.A.]
Generated description
Lux Film S.p.A. was an Italian film production and distribution company active mainly in the mid-20th century, known for producing notable Italian and European cinema.

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_69e2c4c85778819085f5da9af3569ad5 completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a86fdd848190a0f24d30fbaae558 completed April 30, 2026, 12:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fee93682881908d2e820e27e33e94 completed May 22, 2026, 5:50 a.m.
NEDg Description generation batch_6a0fef865e8c81909c338c647f756c65 completed May 22, 2026, 5:54 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff08d9fac81909ea8af6e6b10102a completed May 22, 2026, 5:58 a.m.
Created at: April 18, 2026, 2:24 a.m.