Triple

T34589008
Position Surface form Disambiguated ID Type / Status
Subject Marjorie Liu E888123 entity
Predicate hasWrittenNovel P78982 FINISHED
Object In the Dark of Dreams
"In the Dark of Dreams" is a paranormal romance novel by Marjorie Liu that blends fantasy, myth, and adventure in a story of forbidden love and dark magic.
 E2104102 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: In the Dark of Dreams | Statement: [Marjorie Liu, hasWrittenNovel, In the Dark of Dreams]
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: In the Dark of Dreams
Triple: [Marjorie Liu, hasWrittenNovel, In the Dark of Dreams]
Generated description
"In the Dark of Dreams" is a paranormal romance novel by Marjorie Liu that blends fantasy, myth, and adventure in a story of forbidden love and dark magic.

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_69f349d3bfcc81909874c99e646fb3ea completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f72158f7c081909aed6ea12089998c completed May 3, 2026, 10:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a374106d0ac8190964dab86a1a34800 completed June 21, 2026, 1:40 a.m.
NEDg Description generation batch_6a374208897081909434c3a2e34d2d2f completed June 21, 2026, 1:44 a.m.
NED2 Entity disambiguation (via description) batch_6a3743223b3881909db5005278415166 completed June 21, 2026, 1:49 a.m.
Created at: May 1, 2026, 2:03 a.m.