Triple
T26475300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George D. Hay |
E666013
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
The Solemn Old Judge
The Solemn Old Judge was the radio persona of George D. Hay, the founder and longtime announcer of the Grand Ole Opry, known for his dignified, old-timey storytelling style.
|
E1725806
|
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: The Solemn Old Judge | Statement: [George D. Hay, nickname, The Solemn Old Judge]
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: The Solemn Old Judge Triple: [George D. Hay, nickname, The Solemn Old Judge]
Generated description
The Solemn Old Judge was the radio persona of George D. Hay, the founder and longtime announcer of the Grand Ole Opry, known for his dignified, old-timey storytelling style.
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_69ee883f80dc819090e311b022b78e02 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f612cce1348190861a76259a2b9c85 |
completed | May 2, 2026, 3:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11aee7f320819083e3359d1238ab65 |
completed | May 23, 2026, 1:43 p.m. |
| NEDg | Description generation | batch_6a11b048fe5081909c11c8996d4418af |
completed | May 23, 2026, 1:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11b18b6fdc8190801e1a7b3a296672 |
completed | May 23, 2026, 1:54 p.m. |
Created at: April 27, 2026, 12:22 a.m.