Triple

T30959383
Position Surface form Disambiguated ID Type / Status
Subject Extremely Wicked, Shockingly Evil and Vile E788765 entity
Predicate basedOnAuthor P2806 FINISHED
Object Elizabeth Kendall
Elizabeth Kendall is the former girlfriend of serial killer Ted Bundy who wrote a memoir recounting her relationship with him and her perspective on his crimes.
E1976734 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: Elizabeth Kendall | Statement: [Extremely Wicked, Shockingly Evil and Vile, basedOnAuthor, Elizabeth Kendall]
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: Elizabeth Kendall
Triple: [Extremely Wicked, Shockingly Evil and Vile, basedOnAuthor, Elizabeth Kendall]
Generated description
Elizabeth Kendall is the former girlfriend of serial killer Ted Bundy who wrote a memoir recounting her relationship with him and her perspective on his crimes.

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_69f224c28c1881908c33b45d689f1724 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6934bd47c8190a6d94f2f0b24664c completed May 3, 2026, 12:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b94501b20819083caa54a495c315d completed June 12, 2026, 5:08 a.m.
NEDg Description generation batch_6a2b954314e48190925d87d92d82726c completed June 12, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_6a2b95e46c188190be4513fa3c9705a4 completed June 12, 2026, 5:15 a.m.
Created at: April 29, 2026, 8:54 p.m.