Triple

T29816783
Position Surface form Disambiguated ID Type / Status
Subject Edwina Booth E757128 entity
Predicate spouse P13 FINISHED
Object Reed H. Lewis
Reed H. Lewis was the husband of American film actress Edwina Booth, known for her role in the 1931 adventure film "Trader Horn."
E1926807 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: Reed H. Lewis | Statement: [Edwina Booth, spouse, Reed H. Lewis]
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: Reed H. Lewis
Triple: [Edwina Booth, spouse, Reed H. Lewis]
Generated description
Reed H. Lewis was the husband of American film actress Edwina Booth, known for her role in the 1931 adventure film "Trader Horn."

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_69f2245701c88190ad42415a0956c4ed completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f675637b0c81908fca0623b5feb312 completed May 2, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2870c991f481909ed5fbeb944d86fe completed June 9, 2026, 8 p.m.
NEDg Description generation batch_6a28772713408190b28cee61e4f7df22 completed June 9, 2026, 8:27 p.m.
NED2 Entity disambiguation (via description) batch_6a2877db776c8190b2e93df097522486 completed June 9, 2026, 8:30 p.m.
Created at: April 29, 2026, 5:26 p.m.