Triple

T28303430
Position Surface form Disambiguated ID Type / Status
Subject Nights in Rodanthe E713767 entity
Predicate mainCharacter P1183 FINISHED
Object Adrienne Willis
Adrienne Willis is the emotionally conflicted woman who seeks healing and a fresh start during a transformative seaside encounter in the romantic drama "Nights in Rodanthe."
E1816475 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: Adrienne Willis | Statement: [Nights in Rodanthe, mainCharacter, Adrienne Willis]
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: Adrienne Willis
Triple: [Nights in Rodanthe, mainCharacter, Adrienne Willis]
Generated description
Adrienne Willis is the emotionally conflicted woman who seeks healing and a fresh start during a transformative seaside encounter in the romantic drama "Nights in Rodanthe."

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_69efb524ab688190a1ce7ee7c9520932 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644b4a73c81908a3e56455066d3c8 completed May 2, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632f09fbc8190a25a0983b5edc10a completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a1634256d74819090c95b262c5e6873 completed May 27, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a1635bc8c488190bb284e3caa3c6641 completed May 27, 2026, 12:07 a.m.
Created at: April 27, 2026, 11:36 p.m.