Triple
T23153024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freeheld |
E578368
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | Peter Sollett |
—
|
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: Peter Sollett | Statement: [Freeheld, director, Peter Sollett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Sollett Context triple: [Freeheld, director, Peter Sollett]
-
A.
Peter Sollett
chosen
Peter Sollett is an American film director and screenwriter known for character-driven independent films and offbeat romantic comedies.
-
B.
Andrew Sowle
Andrew Sowle was a 17th-century London Quaker printer and publisher known for producing early colonial American maps and religious works.
-
C.
Christopher Sower
Christopher Sower (Christoph Sauer) was an 18th-century German-American printer and publisher in Pennsylvania, known for producing one of the earliest German-language Bibles in America.
-
D.
Peter Driscoll
Peter Driscoll was a South African-born British thriller novelist best known for his politically charged suspense novels set in Africa.
-
E.
Stephen Gillett
Stephen Gillett is an American technology and business executive known for leadership roles at companies such as Starbucks, Best Buy, and Verily (Alphabet’s life sciences division).
- 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_69e245fb8de081908f0eba7b5fd75bc4 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18efaa1fc81908fb1987dbf732f46 |
completed | April 29, 2026, 4:54 a.m. |
Created at: April 17, 2026, 4:01 p.m.