Triple

T33620160
Position Surface form Disambiguated ID Type / Status
Subject Frank Whaley E861241 entity
Predicate directorOf P537 FINISHED
Object Like Sunday, Like Rain
Like Sunday, Like Rain is an independent drama film about an unexpected bond between a struggling young woman and a gifted, solitary boy in New York City.
 E2059417 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: Like Sunday, Like Rain | Statement: [Frank Whaley, directorOf, Like Sunday, Like Rain]
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: Like Sunday, Like Rain
Triple: [Frank Whaley, directorOf, Like Sunday, Like Rain]
Generated description
Like Sunday, Like Rain is an independent drama film about an unexpected bond between a struggling young woman and a gifted, solitary boy in New York City.

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_69f34980fabc81909819228729a9ca84 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f81ace388190ad2dac7b9da78e19 completed May 3, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3611a74dfc81908c9d42781db03824 completed June 20, 2026, 4:05 a.m.
NEDg Description generation batch_6a3612ddd714819084e5c57e306bb3cd completed June 20, 2026, 4:11 a.m.
NED2 Entity disambiguation (via description) batch_6a361366b0d48190be19bf37db10848b completed June 20, 2026, 4:13 a.m.
Created at: May 1, 2026, 1:41 a.m.