Triple

T29148779
Position Surface form Disambiguated ID Type / Status
Subject Christine Perron E738844 entity
Predicate hasFamilyMember P7844 FINISHED
Object Andrea Perron
Andrea Perron is an American author and lecturer best known for her memoirs about her family’s haunting in the Rhode Island farmhouse that inspired the film “The Conjuring.”
E1854692 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: Andrea Perron | Statement: [Christine Perron, hasFamilyMember, Andrea Perron]
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: Andrea Perron
Triple: [Christine Perron, hasFamilyMember, Andrea Perron]
Generated description
Andrea Perron is an American author and lecturer best known for her memoirs about her family’s haunting in the Rhode Island farmhouse that inspired the film “The Conjuring.”

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_69f07cb46f148190874eb8576a447567 completed April 28, 2026, 9:24 a.m.
NER Named-entity recognition batch_69f662a362088190b474e822a96086e8 completed May 2, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2569ae64208190bec54dbd7d0f9edf completed June 7, 2026, 12:53 p.m.
NEDg Description generation batch_6a256d36d4c081908585afb168ba68cf completed June 7, 2026, 1:08 p.m.
NED2 Entity disambiguation (via description) batch_6a256d9224d48190921ae8c25298119f completed June 7, 2026, 1:09 p.m.
Created at: April 28, 2026, 11:41 a.m.