Triple

T28147282
Position Surface form Disambiguated ID Type / Status
Subject David Mallet E714516 entity
Predicate notableWork P4 FINISHED
Object William and Margaret
"William and Margaret" is a narrative ballad by Scottish poet David Mallet, best known as a classic example of 18th-century English-language ghost-story verse.
 E1806256 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: William and Margaret | Statement: [David Mallet, notableWork, William and Margaret]
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: William and Margaret
Triple: [David Mallet, notableWork, William and Margaret]
Generated description
"William and Margaret" is a narrative ballad by Scottish poet David Mallet, best known as a classic example of 18th-century English-language ghost-story verse.

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_69efd6b033208190bf74f80a147e2092 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f64173f85c8190a0e751a4029052d4 completed May 2, 2026, 6:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15d7a906248190a251e232dde79b77 completed May 26, 2026, 5:26 p.m.
NEDg Description generation batch_6a15d930f12881909f5c9c1effb64334 completed May 26, 2026, 5:32 p.m.
NED2 Entity disambiguation (via description) batch_6a15d9acfe6881909a5ef479b52746c9 completed May 26, 2026, 5:34 p.m.
Created at: April 27, 2026, 9:57 p.m.