Triple

T31589169
Position Surface form Disambiguated ID Type / Status
Subject Kenan family E806041 entity
Predicate hasMember P10 FINISHED
Object Frank Hawkins Kenan
Frank Hawkins Kenan was an American businessman and philanthropist from the prominent Kenan family, known for his significant contributions to education and civic causes, particularly in North Carolina.
 E1969954 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: Frank Hawkins Kenan | Statement: [Kenan family, hasMember, Frank Hawkins Kenan]
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: Frank Hawkins Kenan
Triple: [Kenan family, hasMember, Frank Hawkins Kenan]
Generated description
Frank Hawkins Kenan was an American businessman and philanthropist from the prominent Kenan family, known for his significant contributions to education and civic causes, particularly in North Carolina.

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_69f348d4891c8190b02bae3c8ecb68b7 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a82eb2848190b5329618001a90fd completed May 3, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b5650d73081909be68f53e6aaa383 completed June 12, 2026, 12:44 a.m.
NEDg Description generation batch_6a2b57c1aa048190a551f718ca8c597a completed June 12, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_6a2b711543e08190951560751685fe81 completed June 12, 2026, 2:38 a.m.
Created at: April 30, 2026, 10:27 p.m.