Triple
T38201382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jesus Is on the Main Line |
E1009066
|
entity |
| Predicate | hasTitleVariant |
P455
|
FINISHED |
| Object |
Jesus on the Mainline
Jesus on the Mainline is a traditional gospel blues song, often associated with American roots and spiritual music, that has been widely covered and adapted by various artists.
|
E2260243
|
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: Jesus on the Mainline | Statement: [Jesus Is on the Main Line, hasTitleVariant, Jesus on the Mainline]
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: Jesus on the Mainline Triple: [Jesus Is on the Main Line, hasTitleVariant, Jesus on the Mainline]
Generated description
Jesus on the Mainline is a traditional gospel blues song, often associated with American roots and spiritual music, that has been widely covered and adapted by various artists.
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_69f76dc94fcc8190bd2f55e81f9d6527 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcb12b65248190b903e13147183f39 |
completed | May 7, 2026, 3:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a417b4af6948190896737c6d61f2e4d |
completed | June 28, 2026, 7:51 p.m. |
| NEDg | Description generation | batch_6a417fbe98e88190876febaf49474317 |
completed | June 28, 2026, 8:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41803ffc70819082a30a05c2b95f63 |
completed | June 28, 2026, 8:12 p.m. |
Created at: May 3, 2026, 4:30 p.m.