Triple

T32250554
Position Surface form Disambiguated ID Type / Status
Subject Highway Patrol E823868 entity
Predicate executiveProducer P7225 FINISHED
Object Frederic W. Ziv
Frederic W. Ziv was a pioneering American television producer and syndication executive known for creating and distributing popular mid-20th-century TV series.
 E1999447 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: Frederic W. Ziv | Statement: [Highway Patrol, executiveProducer, Frederic W. Ziv]
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: Frederic W. Ziv
Triple: [Highway Patrol, executiveProducer, Frederic W. Ziv]
Generated description
Frederic W. Ziv was a pioneering American television producer and syndication executive known for creating and distributing popular mid-20th-century TV 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_69f3490cdda88190a9d61e11252a771f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bc36e36c8190a8cbabe17fb627d4 completed May 3, 2026, 3:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46d1755481909bf5acfba547d57f completed June 15, 2026, 12:26 a.m.
NEDg Description generation batch_6a2f47e91cdc81909efa0f51b41c929c completed June 15, 2026, 12:31 a.m.
NED2 Entity disambiguation (via description) batch_6a2f48565c98819082a54783d402d450 completed June 15, 2026, 12:33 a.m.
Created at: May 1, 2026, 12:40 a.m.