Triple

T29368334
Position Surface form Disambiguated ID Type / Status
Subject Buford E744780 entity
Predicate hasNotableBearer P458 FINISHED
Object Buford Ellington
Buford Ellington was an American Democratic politician who served as governor of Tennessee in the 1950s and 1960s.
E1875267 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: Buford Ellington | Statement: [Buford, hasNotableBearer, Buford Ellington]
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: Buford Ellington
Triple: [Buford, hasNotableBearer, Buford Ellington]
Generated description
Buford Ellington was an American Democratic politician who served as governor of Tennessee in the 1950s and 1960s.

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_69f0a79ba954819094597628112c6091 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f669a8aa708190b4bb1e8bc56b0dd7 completed May 2, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a262d4aa3c481908031117d7f38e4f4 completed June 8, 2026, 2:47 a.m.
NEDg Description generation batch_6a26317def3881908eb2e11b7754e1ac completed June 8, 2026, 3:05 a.m.
NED2 Entity disambiguation (via description) batch_6a2635ad095481909c2fbed70b7a5f4c completed June 8, 2026, 3:23 a.m.
Created at: April 28, 2026, 2:25 p.m.