Triple

T33442712
Position Surface form Disambiguated ID Type / Status
Subject European Film Award for Best Cinematographer E856406 entity
Predicate alsoKnownAs P39 FINISHED
Object European Cinematography Award
The European Cinematography Award is a prestigious European Film Award presented annually to honor outstanding achievement in cinematography in European cinema.
E2056308 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: European Cinematography Award | Statement: [European Film Award for Best Cinematographer, alsoKnownAs, European Cinematography Award]
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: European Cinematography Award
Triple: [European Film Award for Best Cinematographer, alsoKnownAs, European Cinematography Award]
Generated description
The European Cinematography Award is a prestigious European Film Award presented annually to honor outstanding achievement in cinematography in 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_69f34971b75881908be360bb041f003c completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6e4a4c19c81908acf2da68afec481 completed May 3, 2026, 6:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35a661b2808190a2c5edd4355a70cd completed June 19, 2026, 8:28 p.m.
NEDg Description generation batch_6a35a6f4ab6c8190a2dfb9106765b8dd completed June 19, 2026, 8:30 p.m.
NED2 Entity disambiguation (via description) batch_6a35a75e1c488190b928a3fa1efaad7c completed June 19, 2026, 8:32 p.m.
Created at: May 1, 2026, 1:37 a.m.