Triple

T23894322
Position Surface form Disambiguated ID Type / Status
Subject For Your Love E600860 entity
Predicate executiveProducer P7225 FINISHED
Object Kerry Lenhart
Kerry Lenhart is an American television writer and producer known for his work on various network series, including serving as an executive producer on the sitcom "For Your Love."
E1607548 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: Kerry Lenhart | Statement: [For Your Love, executiveProducer, Kerry Lenhart]
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: Kerry Lenhart
Triple: [For Your Love, executiveProducer, Kerry Lenhart]
Generated description
Kerry Lenhart is an American television writer and producer known for his work on various network series, including serving as an executive producer on the sitcom "For Your Love."

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_69e295341ac0819080647f2908af793c completed April 17, 2026, 8:16 p.m.
NER Named-entity recognition batch_69f1cdd857d081908740c4abb246c2ba completed April 29, 2026, 9:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f7626c0008190915e8c9b6f80f8bf completed May 21, 2026, 9:16 p.m.
NEDg Description generation batch_6a0f76cc78748190b0f22716094bba19 completed May 21, 2026, 9:19 p.m.
NED2 Entity disambiguation (via description) batch_6a0f77541b948190bb866a8c7c6f5ca9 completed May 21, 2026, 9:21 p.m.
Created at: April 17, 2026, 8:25 p.m.