Triple

T23538741
Position Surface form Disambiguated ID Type / Status
Subject New Directions E577678 entity
Predicate member P10 FINISHED
Object Lauren Zizes
Lauren Zizes is a confident, plus-sized high school student and former wrestler who briefly joins the New Directions glee club on the TV series "Glee," known for her deadpan humor and strong stage presence.
E1690601 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: Lauren Zizes | Statement: [New Directions, member, Lauren Zizes]
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: Lauren Zizes
Triple: [New Directions, member, Lauren Zizes]
Generated description
Lauren Zizes is a confident, plus-sized high school student and former wrestler who briefly joins the New Directions glee club on the TV series "Glee," known for her deadpan humor and strong stage presence.

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_69e245f9d5d08190a4a20004e1784e20 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f1ae19473881909aa65f9d36744502 completed April 29, 2026, 7:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10c0fda8488190a17529d2846b5b1a completed May 22, 2026, 8:47 p.m.
NEDg Description generation batch_6a10c2eee95481908b782308c2a2e5cc completed May 22, 2026, 8:56 p.m.
NED2 Entity disambiguation (via description) batch_6a10c365b12c8190bc9b683ad855c776 completed May 22, 2026, 8:58 p.m.
Created at: April 17, 2026, 6:10 p.m.