Triple

T27534023
Position Surface form Disambiguated ID Type / Status
Subject Mist Over Pendle E695048 entity
Predicate author P4 FINISHED
Object Robert Neill
Robert Neill was a British historical novelist best known for his meticulously researched works set in England’s past, including the acclaimed witchcraft-themed novel "Mist Over Pendle."
E1811348 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: Robert Neill | Statement: [Mist Over Pendle, author, Robert Neill]
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: Robert Neill
Triple: [Mist Over Pendle, author, Robert Neill]
Generated description
Robert Neill was a British historical novelist best known for his meticulously researched works set in England’s past, including the acclaimed witchcraft-themed novel "Mist Over Pendle."

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_69ef538608b081908b9f659bb09d5e0f completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62f5866a48190ad6e727cfe56b234 completed May 2, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1606eefb988190be6b5cc315bcdfcb completed May 26, 2026, 8:47 p.m.
NEDg Description generation batch_6a16132efa1c8190a4b42e8d77aed9b8 completed May 26, 2026, 9:39 p.m.
NED2 Entity disambiguation (via description) batch_6a1613f476b48190b19d51b4dbae3ba7 completed May 26, 2026, 9:43 p.m.
Created at: April 27, 2026, 1:27 p.m.