Triple

T32287425
Position Surface form Disambiguated ID Type / Status
Subject Věra Chytilová E824874 entity
Predicate notableWork P4 FINISHED
Object Fruit of Paradise
Fruit of Paradise is a 1970 Czech experimental film by Věra Chytilová that blends surrealist imagery and allegory to explore themes of temptation and desire.
 E1999839 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: Fruit of Paradise | Statement: [Věra Chytilová, notableWork, Fruit of Paradise]
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: Fruit of Paradise
Triple: [Věra Chytilová, notableWork, Fruit of Paradise]
Generated description
Fruit of Paradise is a 1970 Czech experimental film by Věra Chytilová that blends surrealist imagery and allegory to explore themes of temptation and desire.

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_69f349101b788190b4f14884dc7d1ed2 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bd2f061081909798c04674844492 completed May 3, 2026, 3:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46ee5f34819084ac09df6b56b1b3 completed June 15, 2026, 12:27 a.m.
NEDg Description generation batch_6a2f6f4175e88190b0ed10efdeb386b3 completed June 15, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_6a2f6fd64678819081fba723a6d246e0 completed June 15, 2026, 3:21 a.m.
Created at: May 1, 2026, 12:44 a.m.