Triple
T3696296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carol & Company |
E78466
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Peter Krause |
E354818
|
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: Peter Krause | Statement: [Carol & Company, hasCastMember, Peter Krause]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Krause Context triple: [Carol & Company, hasCastMember, Peter Krause]
-
A.
Peter Krause
chosen
Peter Krause is an American actor best known for his leading roles in television dramas such as Six Feet Under, Sports Night, and Parenthood.
-
B.
Eric Dane
Eric Dane is an American actor best known for his role as Dr. Mark Sloan on the television series "Grey's Anatomy."
-
C.
Donald Faison
Donald Faison is an American actor and comedian best known for his role as Dr. Christopher Turk on the television series "Scrubs."
-
D.
Daniel Baldwin
Daniel Baldwin is an American actor and member of the Baldwin family of performers, known for his roles in film and television as well as his appearances on reality TV.
-
E.
Michael Chiklis
Michael Chiklis is an American actor best known for his Emmy-winning role as tough, corrupt detective Vic Mackey on the television drama "The Shield."
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc50f9ad88190a926042fa73d65dc |
completed | March 8, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e4e5906481909c0d0989dde7743f |
completed | March 14, 2026, 4:32 a.m. |
Created at: March 8, 2026, 3:26 p.m.