Triple
T22741687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Homefront |
E562429
|
entity |
| Predicate | mainCastMember |
P5563
|
FINISHED |
| Object | John Slattery |
—
|
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: John Slattery | Statement: [Homefront, mainCastMember, John Slattery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Slattery Context triple: [Homefront, mainCastMember, John Slattery]
-
A.
John Slattery
chosen
John Slattery is an American actor and director best known for his role as Roger Sterling on the television series "Mad Men."
-
B.
Donald Faison
Donald Faison is an American actor and comedian best known for his role as Dr. Christopher Turk on the television series "Scrubs."
-
C.
Matthew Wiener
Matthew Wiener is a statistician and software developer known for his contributions to the R community, including work on the randomForest package for machine learning.
-
D.
Peter Krause
Peter Krause is an American actor best known for his leading roles in television dramas such as Six Feet Under, Sports Night, and Parenthood.
-
E.
Matthew Weiner
Matthew Weiner is an American television writer, director, and producer best known for creating the critically acclaimed series "Mad Men."
- 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_69e245513a5c81908d5cb471b4fc429d |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1797400fc8190bec26726f434f787 |
completed | April 29, 2026, 3:22 a.m. |
Created at: April 17, 2026, 3:23 p.m.