Triple

T23702536
Position Surface form Disambiguated ID Type / Status
Subject The Twilight Zone (2019 TV series) E585631 entity
Predicate executiveProducer P7225 FINISHED
Object Win Rosenfeld
Win Rosenfeld is an American film and television producer known for his frequent collaborations with Jordan Peele on genre projects such as horror and science fiction.
E1861204 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: Win Rosenfeld | Statement: [The Twilight Zone (2019 TV series), executiveProducer, Win Rosenfeld]
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: Win Rosenfeld
Triple: [The Twilight Zone (2019 TV series), executiveProducer, Win Rosenfeld]
Generated description
Win Rosenfeld is an American film and television producer known for his frequent collaborations with Jordan Peele on genre projects such as horror and science fiction.

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_69e24904bd508190abfcb74855de2918 completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b683edb88190847d34640b6cfabe completed April 29, 2026, 7:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a25a829d6b08190af6c336fdd7f38c8 completed June 7, 2026, 5:19 p.m.
NEDg Description generation batch_6a25aca216088190b6e106c9172f638c completed June 7, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_6a25b14c4f088190afc5aa0e3d78ab43 completed June 7, 2026, 5:58 p.m.
Created at: April 17, 2026, 6:53 p.m.