Triple

T33032185
Position Surface form Disambiguated ID Type / Status
Subject Julie Barer E845192 entity
Predicate represents P129 FINISHED
Object Erika Swyler
Erika Swyler is an American novelist best known for her literary fiction that blends family drama with elements of mystery and the uncanny, including the acclaimed novel "The Book of Speculation."
E2090355 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: Erika Swyler | Statement: [Julie Barer, represents, Erika Swyler]
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: Erika Swyler
Triple: [Julie Barer, represents, Erika Swyler]
Generated description
Erika Swyler is an American novelist best known for her literary fiction that blends family drama with elements of mystery and the uncanny, including the acclaimed novel "The Book of Speculation."

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_69f34950749c8190ae05cd27adb16d58 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d2e55c4c8190bd69be56132578e0 completed May 3, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36f9a491488190bf06f4b87a71c219 completed June 20, 2026, 8:35 p.m.
NEDg Description generation batch_6a36fa3f99f08190b96e65347f9dcc63 completed June 20, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a36faa625708190b37bf417c721e5bc completed June 20, 2026, 8:40 p.m.
Created at: May 1, 2026, 1:24 a.m.