Triple

T34271193
Position Surface form Disambiguated ID Type / Status
Subject Kristen Bouchard E879319 entity
Predicate hasMother P1909 FINISHED
Object Sheryl Luria
Sheryl Luria is a fictional character known primarily as the mother of Kristen Bouchard in the television series "Evil."
E1751620 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: Sheryl Luria | Statement: [Kristen Bouchard, hasMother, Sheryl Luria]
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: Sheryl Luria
Triple: [Kristen Bouchard, hasMother, Sheryl Luria]
Generated description
Sheryl Luria is a fictional character known primarily as the mother of Kristen Bouchard in the television series "Evil."

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_69f349b4f5fc819094b441d18e95e5f1 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f712ce4a0c819087886c61df83f219 completed May 3, 2026, 9:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a37faf67f2881908255bf2aad722cb0 completed June 21, 2026, 2:53 p.m.
NEDg Description generation batch_6a37fb873f388190b294ac802eebd386 completed June 21, 2026, 2:56 p.m.
NED2 Entity disambiguation (via description) batch_6a37fbdf0fec8190a0f2ad8581c29f58 completed June 21, 2026, 2:57 p.m.
Created at: May 1, 2026, 1:56 a.m.