Triple

T38242001
Position Surface form Disambiguated ID Type / Status
Subject Chastain Park Memorial Hospital E1013790 entity
Predicate hasStaffCharacter P61558 FINISHED
Object Randolph Bell
Randolph Bell is a central character and initially flawed but evolving senior surgeon and hospital executive on the medical drama series "The Resident."
E2270541 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: Randolph Bell | Statement: [Chastain Park Memorial Hospital, hasStaffCharacter, Randolph Bell]
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: Randolph Bell
Triple: [Chastain Park Memorial Hospital, hasStaffCharacter, Randolph Bell]
Generated description
Randolph Bell is a central character and initially flawed but evolving senior surgeon and hospital executive on the medical drama series "The Resident."

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_69f76dd72a248190a5fe18db2bd1eb15 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69fcb180f138819090972487d009ff28 completed May 7, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41cc9393088190ada4612c1e9ffdbb completed June 29, 2026, 1:38 a.m.
NEDg Description generation batch_6a41cdd269d881908e636622d74eee6e completed June 29, 2026, 1:43 a.m.
NED2 Entity disambiguation (via description) batch_6a41ce70fe0c8190a627207b8b97ddc5 completed June 29, 2026, 1:46 a.m.
Created at: May 3, 2026, 4:30 p.m.