Triple
T21040221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Sweeney |
E518300
|
entity |
| Predicate | portrays |
P264
|
FINISHED |
| Object | George Carter |
—
|
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: George Carter | Statement: [The Sweeney, portrays, George Carter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Carter Context triple: [The Sweeney, portrays, George Carter]
-
A.
George Carter
George Carter was a professional basketball player best known for his standout career in the American Basketball Association (ABA), including his time with the Virginia Squires.
-
B.
George Carter
George Carter was a prominent Virginia landowner and member of the influential Carter family in the early 19th century.
-
C.
George Carter
George Carter was a British aircraft designer best known for creating the Gloster Meteor, the Allies’ first operational jet fighter during World War II.
-
D.
George Carter
chosen
George Carter is a tough, streetwise detective sergeant in the British television crime drama "The Sweeney," known for his no-nonsense approach to policing alongside his partner Jack Regan.
-
E.
H. E. Carter
H. E. Carter was an American biochemist and academic known for his influential research and mentorship, including supervising future Nobel laureate Phillip A. Sharp.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fceed9148190903adb3b55f65242 |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:14 p.m.