Triple
T3715799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One Eight Seven |
E81526
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mel Efros |
E64553
|
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: Mel Efros | Statement: [One Eight Seven, producer, Mel Efros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mel Efros Context triple: [One Eight Seven, producer, Mel Efros]
-
A.
Alexei Efros
chosen
Alexei Efros is a prominent computer scientist known for his influential work in computer vision and computational photography.
-
B.
Jonathon Shlens
Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
-
C.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
-
D.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
E.
Ruslan Salakhutdinov
Ruslan Salakhutdinov is a prominent machine learning researcher known for his contributions to deep learning and probabilistic graphical models, and for serving as Director of AI Research at Apple and a professor at Carnegie Mellon University.
- 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_69ad8b1a81588190b3f27a5483bb610e |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc9cf77dc819098979094172d82d1 |
completed | March 8, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4ce0f690c819091d9caf9271f9bbd |
completed | March 14, 2026, 2:55 a.m. |
Created at: March 8, 2026, 3:33 p.m.