Triple
T23816046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Seventh Seal (album) |
E589096
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Troy Miller
Troy Miller is a British drummer, producer, and songwriter known for his work across jazz, soul, and pop with artists such as Amy Winehouse, Laura Mvula, and Gregory Porter.
|
E1610754
|
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: Troy Miller | Statement: [The Seventh Seal (album), producer, Troy Miller]
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: Troy Miller Triple: [The Seventh Seal (album), producer, Troy Miller]
Generated description
Troy Miller is a British drummer, producer, and songwriter known for his work across jazz, soul, and pop with artists such as Amy Winehouse, Laura Mvula, and Gregory Porter.
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_69e25d18619081909c7fb89d8926f14a |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c7aa91cc81909cc41db5c9c71fe5 |
completed | April 29, 2026, 8:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f761013e08190b5e9375726fc854a |
completed | May 21, 2026, 9:16 p.m. |
| NEDg | Description generation | batch_6a0f77cae9dc8190b324ac8deeac83c4 |
completed | May 21, 2026, 9:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f78df8c9c81908eb3912b212862f9 |
completed | May 21, 2026, 9:27 p.m. |
Created at: April 17, 2026, 7:58 p.m.