Triple

T32492538
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth Jean Philipps E830426 entity
Predicate portrayedCharacter P1668 FINISHED
Object Laurie Keller
Laurie Keller is a quirky, optimistic, and somewhat scatterbrained best friend character from the TV series "Cougar Town," known for her close relationship with protagonist Jules Cobb.
E2243528 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: Laurie Keller | Statement: [Elizabeth Jean Philipps, portrayedCharacter, Laurie Keller]
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: Laurie Keller
Triple: [Elizabeth Jean Philipps, portrayedCharacter, Laurie Keller]
Generated description
Laurie Keller is a quirky, optimistic, and somewhat scatterbrained best friend character from the TV series "Cougar Town," known for her close relationship with protagonist Jules Cobb.

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_69f34920aa4081908d8fb0277414b911 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c4087e048190884d3902fdc81aa5 completed May 3, 2026, 3:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a40f15e67c881909176789bd30dd41b completed June 28, 2026, 10:03 a.m.
NEDg Description generation batch_6a40f23ecbc081909576b02690c2cc92 completed June 28, 2026, 10:06 a.m.
NED2 Entity disambiguation (via description) batch_6a40f2dd54488190b0da6cb656706f54 completed June 28, 2026, 10:09 a.m.
Created at: May 1, 2026, 12:59 a.m.