Triple

T30935360
Position Surface form Disambiguated ID Type / Status
Subject Audrey Totter E788106 entity
Predicate role P268 FINISHED
Object Nurse Wilcox in Medical Center
Nurse Wilcox in *Medical Center* is a recurring character, a dedicated and capable hospital nurse portrayed by actress Audrey Totter in the American medical drama television series.
 E1939302 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: Nurse Wilcox in Medical Center | Statement: [Audrey Totter, role, Nurse Wilcox in Medical Center]
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: Nurse Wilcox in Medical Center
Triple: [Audrey Totter, role, Nurse Wilcox in Medical Center]
Generated description
Nurse Wilcox in *Medical Center* is a recurring character, a dedicated and capable hospital nurse portrayed by actress Audrey Totter in the American medical drama television series.

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_69f224c0b7fc819090cb89df60d23653 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f692e442e4819084cbd7e63cc420d4 completed May 3, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28e47400448190bd0c471c594862a6 completed June 10, 2026, 4:13 a.m.
NEDg Description generation batch_6a28e8a299908190a16f145e901f8edd completed June 10, 2026, 4:31 a.m.
NED2 Entity disambiguation (via description) batch_6a28e91bbbcc8190bf420aaed9cf4b8a completed June 10, 2026, 4:33 a.m.
Created at: April 29, 2026, 8:52 p.m.