Triple

T28404193
Position Surface form Disambiguated ID Type / Status
Subject David Manners E719477 entity
Predicate birthName P65 FINISHED
Object Rauff de Ryther Duan Acklom
Rauff de Ryther Duan Acklom, better known as David Manners, was a Canadian-born American actor prominent in early 1930s Hollywood films, including classic horror and adventure movies.
 E1817144 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: Rauff de Ryther Duan Acklom | Statement: [David Manners, birthName, Rauff de Ryther Duan Acklom]
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: Rauff de Ryther Duan Acklom
Triple: [David Manners, birthName, Rauff de Ryther Duan Acklom]
Generated description
Rauff de Ryther Duan Acklom, better known as David Manners, was a Canadian-born American actor prominent in early 1930s Hollywood films, including classic horror and adventure movies.

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_69eff6efd1b08190ae3cefd4f11388a2 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64d6e29388190a285f0ff5bc70a3f completed May 2, 2026, 7:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1633124e148190a3c5cabf8d1a38f6 completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a1634c284bc8190a09d836655486dc7 completed May 27, 2026, 12:03 a.m.
NED2 Entity disambiguation (via description) batch_6a1638ab3f5c8190be17ee9121039cd1 completed May 27, 2026, 12:19 a.m.
Created at: April 28, 2026, 1:22 a.m.