Triple
T12207393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Convolutional GAN |
E290869
|
entity |
| Predicate | introducedBy |
P513
|
FINISHED |
| Object | Luke Metz |
E620418
|
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: Luke Metz | Statement: [Deep Convolutional GAN, introducedBy, Luke Metz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luke Metz Context triple: [Deep Convolutional GAN, introducedBy, Luke Metz]
-
A.
Luke Metz
chosen
Luke Metz is a machine learning researcher known for his work on generative models and deep learning, often collaborating with Alec Radford.
-
B.
Lee Zahler
Lee Zahler was an American film composer and musical director known for scoring numerous serials and B-movies during the 1930s and 1940s.
-
C.
Sam Koppelman
Sam Koppelman is an American writer and political speechwriter known for co-authoring books with figures like Beto O’Rourke and for his work on voting rights and democracy.
-
D.
Aaron Korsh
Aaron Korsh is an American television writer and producer best known for creating the legal drama series "Suits."
-
E.
Michael Gilio
Michael Gilio is an American screenwriter and filmmaker best known for co-writing the fantasy adventure film "Dungeons & Dragons: Honor Among Thieves."
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c7d8f5c8190a46e9caa2a920fa9 |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a8c69308190bffae7b38cc5620b |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:51 p.m.