Triple

T35666487
Position Surface form Disambiguated ID Type / Status
Subject Jury Duty (2023 TV series) E1030576 entity
Predicate starring P1507 FINISHED
Object Alan Barinholtz
Alan Barinholtz is an American actor and attorney known for his comedic roles in film and television, including his appearance in the docu-comedy series "Jury Duty."
E2154256 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: Alan Barinholtz | Statement: [Jury Duty (2023 TV series), starring, Alan Barinholtz]
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: Alan Barinholtz
Triple: [Jury Duty (2023 TV series), starring, Alan Barinholtz]
Generated description
Alan Barinholtz is an American actor and attorney known for his comedic roles in film and television, including his appearance in the docu-comedy series "Jury Duty."

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_69f76e09f87881909c954aaac176c34f completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f79fac9a748190bbead51a19556c63 completed May 3, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3885e12fa481909f59f3265f2cc1eb completed June 22, 2026, 12:46 a.m.
NEDg Description generation batch_6a3886c807fc81908b54dc825e1770a6 completed June 22, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_6a388786698c8190a77c02a874d0d790 completed June 22, 2026, 12:53 a.m.
Created at: May 3, 2026, 4:05 p.m.