Triple

T32865863
Position Surface form Disambiguated ID Type / Status
Subject African Free School E840645 entity
Predicate notableAlumnus P304 FINISHED
Object Charles L. Reason
Charles L. Reason was a pioneering African American educator, mathematician, and abolitionist who became one of the first Black professors at a predominantly white college in the United States.
E2025756 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: Charles L. Reason | Statement: [African Free School, notableAlumnus, Charles L. Reason]
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: Charles L. Reason
Triple: [African Free School, notableAlumnus, Charles L. Reason]
Generated description
Charles L. Reason was a pioneering African American educator, mathematician, and abolitionist who became one of the first Black professors at a predominantly white college in the United States.

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_69f34942465c819099b3fb47f9044f58 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6ceb9fc288190b4f187a33010c4e6 completed May 3, 2026, 4:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bd1311188190835de335975311b8 completed June 19, 2026, 3:52 a.m.
NEDg Description generation batch_6a34be2961548190996f1946ea749efe completed June 19, 2026, 3:57 a.m.
NED2 Entity disambiguation (via description) batch_6a34bf150f8081909b249cb22230eb2e completed June 19, 2026, 4:01 a.m.
Created at: May 1, 2026, 1:17 a.m.