Triple
T37793685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Cruise as Vincent |
E942147
|
entity |
| Predicate | mainCounterpartPortrayedBy |
P129059
|
FINISHED |
| Object | Jamie Foxx |
—
|
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: Jamie Foxx | Statement: [Tom Cruise as Vincent, mainCounterpartPortrayedBy, Jamie Foxx]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCounterpartPortrayedBy Context triple: [Tom Cruise as Vincent, mainCounterpartPortrayedBy, Jamie Foxx]
-
A.
partnerPortrayedBy
chosen
Indicates that one entity is the actor or performer who portrays the partner or counterpart of another entity in a work.
-
B.
fictionalCounterpartIn
Indicates that one entity serves as a fictional analogue or stand-in for another entity within a specified work or fictional universe.
-
C.
hasCounterpartName
Indicates that an entity has an alternative or corresponding name used as its counterpart in another context, system, or representation.
-
D.
hasCounterpart
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
E.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
- F. None of above.
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_69f76ee6f1f4819091e2cf9c9e6aee19 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fd974d75e08190af46b1d608769f3b |
completed | May 8, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69fd94ff792c8190bedf4a639d3da809 |
completed | May 8, 2026, 7:47 a.m. |
Created at: May 3, 2026, 4:19 p.m.