Triple

T27368623
Position Surface form Disambiguated ID Type / Status
Subject Ethel Gordon Fenwick E690243 entity
Predicate spouse P13 FINISHED
Object Dr. Bedford Fenwick
Dr. Bedford Fenwick was a British physician and medical reformer known for his role in advancing professional nursing alongside his wife, Ethel Gordon Fenwick.
 E1771091 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: Dr. Bedford Fenwick | Statement: [Ethel Gordon Fenwick, spouse, Dr. Bedford Fenwick]
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: Dr. Bedford Fenwick
Triple: [Ethel Gordon Fenwick, spouse, Dr. Bedford Fenwick]
Generated description
Dr. Bedford Fenwick was a British physician and medical reformer known for his role in advancing professional nursing alongside his wife, Ethel Gordon Fenwick.

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_69ef51ff826081909e42c8e2bfb97941 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62c5f6bf48190b5ca045b90b14c54 completed May 2, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7e15a788190b6cd108853c80234 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a9ef43ac819097d5c47108692c15 completed May 24, 2026, 7:34 a.m.
NED2 Entity disambiguation (via description) batch_6a12ab2c6840819085f11be72866c959 completed May 24, 2026, 7:39 a.m.
Created at: April 27, 2026, 12:18 p.m.