Triple
T13156284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paula Wagner |
E312597
|
entity |
| Predicate | represented |
P192
|
FINISHED |
| Object | Keanu Reeves |
E65178
|
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: Keanu Reeves | Statement: [Paula Wagner, represented, Keanu Reeves]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keanu Reeves Context triple: [Paula Wagner, represented, Keanu Reeves]
-
A.
Keanu Reeves
chosen
Keanu Reeves is a Canadian actor and producer known for his roles in blockbuster action and science fiction films such as The Matrix and John Wick franchises.
-
B.
Ed Norton
Ed Norton is the jovial, dim-witted sewer worker and best friend of Ralph Kramden in the classic American television sitcom "The Honeymooners."
-
C.
Nicolas Cage
Nicolas Cage is an American actor known for his intense and eclectic performances across action, drama, and independent films.
-
D.
Eric Bana
Eric Bana is an Australian actor known for his versatile performances in films such as "Hulk," "Munich," and "Troy."
-
E.
Reeves
Reeves is a surname of English origin borne by numerous notable individuals across sports, entertainment, and other fields.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c06ccb881909390df18e1a6f7ed |
completed | April 10, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eaf06f408190949f9ed5e899815b |
completed | May 3, 2026, 6:28 a.m. |
Created at: April 9, 2026, 9:12 p.m.