Triple
T18253098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greg Simmonds |
E437148
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Greg Simmonds |
—
|
NE NERFINISHED |
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: Greg Simmonds | Statement: [Greg Simmonds, hasName, Greg Simmonds]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greg Simmonds Context triple: [Greg Simmonds, hasName, Greg Simmonds]
-
A.
Greg Simmonds
chosen
Greg Simmonds is a wealthy and influential arms dealer who becomes the target of a covert espionage operation in the action-comedy film "Operation Fortune: Ruse de Guerre."
-
B.
Jake Simmonds
Jake Simmonds is a character from the Doctor Who universe who appears in the two-part story involving the rise of the Cybermen in a parallel Earth.
-
C.
Michael Simmonds
Michael Simmonds is an American cinematographer known for his work on independent films and collaborations with director Sean Baker.
-
D.
Brian Sims
Brian Sims is an American civil rights attorney, LGBTQ+ activist, and former Pennsylvania state legislator known for being the first openly gay elected state representative in Pennsylvania.
-
E.
Jay Simms
Jay Simms was an American screenwriter best known for his work on mid-20th-century science fiction and genre films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd81ea3481909d96b5399f7a32b3 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 10:33 a.m.