Triple
T11307191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | It Won’t Be Soon Before Long |
E267746
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mark Endert |
E740831
|
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 Endert | Statement: [It Won’t Be Soon Before Long, producer, Mark Endert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Endert Context triple: [It Won’t Be Soon Before Long, producer, Mark Endert]
-
A.
Mark Endert
chosen
Mark Endert is an American music producer and mixer known for his work on numerous mainstream pop and rock albums and hit singles.
-
B.
Mark Wendland
Mark Wendland is an American scenic designer known for his innovative and visually striking sets for Broadway productions and other theatrical works.
-
C.
Michael Grunst
Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
-
D.
Michael Endres
Michael Endres is a German classical pianist renowned for his interpretations of Schubert and other Romantic repertoire, as well as for his extensive recording work.
-
E.
Christopher Lennertz
Christopher Lennertz is an American composer best known for his film, television, and video game scores, including work on major comedies, action films, and popular series like Supernatural.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9bf87d88190904c2d174578ebbf |
completed | April 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e8b19b1c8190bc9147a9fc73e35b |
completed | April 20, 2026, 8:49 a.m. |
Created at: April 8, 2026, 9:32 p.m.