Triple

T28550140
Position Surface form Disambiguated ID Type / Status
Subject Jenna Leigh Green E722861 entity
Predicate alsoKnownAs P39 FINISHED
Object Jennifer Leigh Greenberg
Jennifer Leigh Greenberg is an American actress and singer best known for her role as Libby Chessler on the television series "Sabrina the Teenage Witch."
E1830422 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: Jennifer Leigh Greenberg | Statement: [Jenna Leigh Green, alsoKnownAs, Jennifer Leigh Greenberg]
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: Jennifer Leigh Greenberg
Triple: [Jenna Leigh Green, alsoKnownAs, Jennifer Leigh Greenberg]
Generated description
Jennifer Leigh Greenberg is an American actress and singer best known for her role as Libby Chessler on the television series "Sabrina the Teenage Witch."

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_69f01a60204481909af1bb76247b8221 completed April 28, 2026, 2:24 a.m.
NER Named-entity recognition batch_69f65010b7588190a38981fc7c925ed3 completed May 2, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf2c629481908123c7b8c4404a10 completed June 1, 2026, 12:15 a.m.
NEDg Description generation batch_6a1cd03986848190a322d5273d0164d0 completed June 1, 2026, 12:20 a.m.
NED2 Entity disambiguation (via description) batch_6a249466d5b08190bd3886ef517cb367 completed June 6, 2026, 9:43 p.m.
Created at: April 28, 2026, 3:42 a.m.