Triple
T22427315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gordon Clark |
E554405
|
entity |
| Predicate | businessPartner |
P282
|
FINISHED |
| Object | Cameron Howe |
—
|
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: Cameron Howe | Statement: [Gordon Clark, businessPartner, Cameron Howe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cameron Howe Context triple: [Gordon Clark, businessPartner, Cameron Howe]
-
A.
Cameron Howe
chosen
Cameron Howe is a brilliant, rebellious computer programmer and visionary in the tech industry, best known as a central character in the television series "Halt and Catch Fire."
-
B.
Ash Howes
Ash Howes is a British record producer and mix engineer known for his work with major pop artists and chart-topping albums.
-
C.
Greg Howe
Greg Howe is an American guitarist renowned for his virtuosic fusion of rock, jazz, and funk, and his influential work as both a solo artist and session musician.
-
D.
Drake Sather
Drake Sather was an American stand-up comedian and television writer best known for co-creating the character that inspired the film "Zoolander."
-
E.
Dylan Posa
Dylan Posa is a musician best known as a former member of the experimental rock band Cheer-Accident.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a2e438481908d43026727afa709 |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.