Triple
T11760346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam Secretary |
E279638
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | David Grae |
E282949
|
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: David Grae | Statement: [Madam Secretary, executiveProducer, David Grae]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Grae Context triple: [Madam Secretary, executiveProducer, David Grae]
-
A.
David Grae
chosen
David Grae is a television writer and producer best known for his work on political and legal drama series.
-
B.
David Gordon
David Gordon is an economist known for his influential work in macroeconomic theory and policy, including collaborations with Robert J. Barro.
-
C.
Robert Stivers
Robert Stivers is an American Republican politician who serves as the President of the Kentucky Senate and has been a key legislative leader in the state.
-
D.
Jay Graydon
Jay Graydon is an American guitarist, songwriter, and Grammy-winning producer known for his sophisticated pop and jazz fusion work with artists such as Steely Dan, Al Jarreau, and Airplay.
-
E.
David Eigen
David Eigen is a computer scientist and researcher known for his contributions to deep learning and computer vision, including early work on convolutional neural networks for image understanding.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a52386708190b744746a2db37495 |
completed | April 10, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f01a3dfd1081908221c8061931282b |
completed | April 28, 2026, 2:23 a.m. |
Created at: April 8, 2026, 9:41 p.m.