Triple
T15169772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Bay |
E362452
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Aaron Yanes |
E1051892
|
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: Aaron Yanes | Statement: [The Bay, editedBy, Aaron Yanes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aaron Yanes Context triple: [The Bay, editedBy, Aaron Yanes]
-
A.
Aaron Yanes
chosen
Aaron Yanes is a film editor known for his work on projects such as the television film "The Wizard of Lies."
-
B.
Greg Yaitanes
Greg Yaitanes is an American television director and producer known for his work on high-profile series such as House of the Dragon and House.
-
C.
Andrew Solt
Andrew Solt was a Hungarian-American screenwriter and producer best known for his work on mid-20th-century films and later for creating music-related documentaries and television programs.
-
D.
Tod Andrews
Tod Andrews was an American film, television, and stage actor active in the mid-20th century, known for his character roles across a variety of popular series and movies.
-
E.
Luke Metz
Luke Metz is a machine learning researcher known for his work on generative models and deep learning, often collaborating with Alec Radford.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0064ec56481909f11fa6e5686f076 |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec889c3408190bdfc75ce72dd5a62 |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 3:08 a.m.