Triple

T35525043
Position Surface form Disambiguated ID Type / Status
Subject Porsha Williams E1026648 entity
Predicate participantIn P149 FINISHED
Object The Celebrity Apprentice
The Celebrity Apprentice is a reality television competition series in which celebrities complete business-related tasks to raise money for charity while avoiding elimination in the boardroom.
E2146889 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: The Celebrity Apprentice | Statement: [Porsha Williams, participantIn, The Celebrity Apprentice]
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: The Celebrity Apprentice
Triple: [Porsha Williams, participantIn, The Celebrity Apprentice]
Generated description
The Celebrity Apprentice is a reality television competition series in which celebrities complete business-related tasks to raise money for charity while avoiding elimination in the boardroom.

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_69f76dfe78b081908e2b14cb88dd8c00 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f797cde47081909ad77ec0e4b32d17 completed May 3, 2026, 6:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a385bc67de88190bd838cb0525bd6d0 completed June 21, 2026, 9:46 p.m.
NEDg Description generation batch_6a385c35b6cc8190b6fc00bc793c1c15 completed June 21, 2026, 9:48 p.m.
NED2 Entity disambiguation (via description) batch_6a385c8fe0e881908f84cd1f8670b699 completed June 21, 2026, 9:50 p.m.
Created at: May 3, 2026, 4:04 p.m.