Triple
T15225192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lie Down in the Light |
E363858
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mark Nevers |
E1140606
|
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: Mark Nevers | Statement: [Lie Down in the Light, producer, Mark Nevers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Nevers Context triple: [Lie Down in the Light, producer, Mark Nevers]
-
A.
Mark Nevers
chosen
Mark Nevers is an American record producer and engineer best known for his work in the alt-country and indie scenes, particularly with artists like Lambchop and Bonnie "Prince" Billy.
-
B.
Mark Suter
Mark Suter is a percussionist known for his work in contemporary and world music, including performances with the Silk Road Ensemble.
-
C.
Don Brautigam
Don Brautigam was an American illustrator best known for his striking, realistic cover art for horror and thriller novels, including works by Stephen King.
-
D.
Grant Bardsley
Grant Bardsley is a British voice actor best known for voicing the protagonist Taran in Disney’s animated film "The Black Cauldron."
-
E.
Kyle T. Heffner
Kyle T. Heffner is an American character actor known for supporting roles in films and television since the 1980s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078a9318819081db3b7bcc28e04f |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5edca5c8190827788324a9e886d |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:12 a.m.