Triple
T12090215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep State |
E287921
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Simon Maxwell |
E962518
|
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: Simon Maxwell | Statement: [Deep State, executiveProducer, Simon Maxwell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Simon Maxwell Context triple: [Deep State, executiveProducer, Simon Maxwell]
-
A.
Simon Maxwell
chosen
Simon Maxwell is a creator best known for developing the project or work titled "Deep State."
-
B.
Jonathan Dixon Maxwell
Jonathan Dixon Maxwell was an early American automotive pioneer and engineer best known for co-founding and leading the Maxwell Motor Company, a precursor to Chrysler.
-
C.
Simon Gage
Simon Gage is a notable individual recognized for achievements significant enough to be distinctly associated with the surname Gage.
-
D.
Maxwell Sheffield
Maxwell Sheffield is a wealthy, widowed Broadway producer and father of three who becomes the employer and eventual love interest of Fran Fine in the sitcom "The Nanny."
-
E.
Simon Nye
Simon Nye is a British television writer and screenwriter best known for creating the sitcom "Men Behaving Badly" and adapting various literary works for TV.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915161f848190a6355c1e372eadaa |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a6d74888190aab150f1ceb2e9f1 |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:48 p.m.