Triple

T33977590
Position Surface form Disambiguated ID Type / Status
Subject Cyrus G. Luce E871182 entity
Predicate succeededBy P78 FINISHED
Object Edwin B. Winans
Edwin B. Winans was an American politician who served as the 22nd governor of Michigan in the late 19th century.
E2085124 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: Edwin B. Winans | Statement: [Cyrus G. Luce, succeededBy, Edwin B. Winans]
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: Edwin B. Winans
Triple: [Cyrus G. Luce, succeededBy, Edwin B. Winans]
Generated description
Edwin B. Winans was an American politician who served as the 22nd governor of Michigan in the late 19th century.

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_69f3499da0188190ab1a4ff06fb06a2a completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7032e808081909c037b8538b83bc5 completed May 3, 2026, 8:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36cc6860088190bde4ee1772e05936 completed June 20, 2026, 5:22 p.m.
NEDg Description generation batch_6a36cd0239908190bd16360a88d43607 completed June 20, 2026, 5:25 p.m.
NED2 Entity disambiguation (via description) batch_6a36cd74cf1c8190bfdb1ad2b77726fb completed June 20, 2026, 5:27 p.m.
Created at: May 1, 2026, 1:50 a.m.