Triple

T33655065
Position Surface form Disambiguated ID Type / Status
Subject The Broken Hearts Gallery E862198 entity
Predicate director P255 FINISHED
Object Natalie Krinsky
Natalie Krinsky is a Canadian-American screenwriter and film director best known for writing and directing the romantic comedy film "The Broken Hearts Gallery."
E2062498 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: Natalie Krinsky | Statement: [The Broken Hearts Gallery, director, Natalie Krinsky]
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: Natalie Krinsky
Triple: [The Broken Hearts Gallery, director, Natalie Krinsky]
Generated description
Natalie Krinsky is a Canadian-American screenwriter and film director best known for writing and directing the romantic comedy film "The Broken Hearts Gallery."

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_69f349840ba881908e3bfce536aeb92b completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f9c6f8808190b43b3f1a1bc71bf2 completed May 3, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a363c89fff48190911ebe5b0559bd32 completed June 20, 2026, 7:08 a.m.
NEDg Description generation batch_6a3643f5b9dc8190a79e435f96ace787 completed June 20, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a36452496188190b0148e0857e88922 completed June 20, 2026, 7:45 a.m.
Created at: May 1, 2026, 1:42 a.m.