Triple

T26143238
Position Surface form Disambiguated ID Type / Status
Subject Claude Renoir E659579 entity
Predicate workedOn P3 FINISHED
Object Picnic on the Grass
Picnic on the Grass is a 1959 French-Italian satirical comedy film directed by Jean Renoir that gently critiques modern society and rationalism through the story of a wealthy scientist’s countryside outing.
 E1710216 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: Picnic on the Grass | Statement: [Claude Renoir, workedOn, Picnic on the Grass]
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: Picnic on the Grass
Triple: [Claude Renoir, workedOn, Picnic on the Grass]
Generated description
Picnic on the Grass is a 1959 French-Italian satirical comedy film directed by Jean Renoir that gently critiques modern society and rationalism through the story of a wealthy scientist’s countryside outing.

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_69ee5bc496a88190af7deb7ab5e081de completed April 26, 2026, 6:39 p.m.
NER Named-entity recognition batch_69f60be63218819089ef0c5eb968db2f completed May 2, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11276b010481909f8911733d49f613 completed May 23, 2026, 4:04 a.m.
NEDg Description generation batch_6a114a9956d081909186eb0e116f1644 completed May 23, 2026, 6:35 a.m.
NED2 Entity disambiguation (via description) batch_6a114b3da41481908fede75cc82bdf99 completed May 23, 2026, 6:37 a.m.
Created at: April 26, 2026, 8:21 p.m.