Triple
T8444541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oak |
E199639
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object |
Warren Felder
Warren Felder, better known by his professional name Oak, is an American record producer and songwriter recognized for his work with numerous prominent R&B and pop artists.
|
E734601
|
NE FINISHED |
How this triple was built (4 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: Warren Felder | Statement: [Oak, birthName, Warren Felder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warren Felder Context triple: [Oak, birthName, Warren Felder]
-
A.
Bryan DeWitt
Bryan DeWitt is a person notable enough to be recognized as a bearer of the De Witt surname.
-
B.
Stephen Coleman
Stephen Coleman is a music producer known for his work on major television soundtracks, including the third season of HBO's Game of Thrones.
-
C.
Ben D. Waisbren
Ben D. Waisbren is a film producer known for financing and producing major studio and independent movies.
-
D.
Jeffrey M. Werner
Jeffrey M. Werner is a film editor best known for his work on acclaimed independent and studio features, including the comedy-drama "The Kids Are All Right."
-
E.
Daniel L. Fapp
Daniel L. Fapp was an American cinematographer known for his work on numerous Hollywood films, including the Oscar-winning West Side Story.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Warren Felder Triple: [Oak, birthName, Warren Felder]
Generated description
Warren Felder, better known by his professional name Oak, is an American record producer and songwriter recognized for his work with numerous prominent R&B and pop artists.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Warren Felder Target entity description: Warren Felder, better known by his professional name Oak, is an American record producer and songwriter recognized for his work with numerous prominent R&B and pop artists.
-
A.
Bryan DeWitt
Bryan DeWitt is a person notable enough to be recognized as a bearer of the De Witt surname.
-
B.
Stephen Coleman
Stephen Coleman is a music producer known for his work on major television soundtracks, including the third season of HBO's Game of Thrones.
-
C.
Ben D. Waisbren
Ben D. Waisbren is a film producer known for financing and producing major studio and independent movies.
-
D.
Jeffrey M. Werner
Jeffrey M. Werner is a film editor best known for his work on acclaimed independent and studio features, including the comedy-drama "The Kids Are All Right."
-
E.
Daniel L. Fapp
Daniel L. Fapp was an American cinematographer known for his work on numerous Hollywood films, including the Oscar-winning West Side Story.
- F. None of above. chosen
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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe3122cfc8190ac6103fa4e4a7c45 |
completed | March 31, 2026, 3:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1dac8cb08190b74985a6ba3c938f |
completed | April 2, 2026, 7:41 a.m. |
| NEDg | Description generation | batch_69ce1f3729e4819084600862b53c94a8 |
completed | April 2, 2026, 7:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce1fdde53c8190b356950f878b6b70 |
completed | April 2, 2026, 7:50 a.m. |
Created at: March 30, 2026, 6:09 p.m.