Triple
T16008403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Kosinski |
E388275
|
entity |
| Predicate | workedWith |
P398
|
FINISHED |
| Object | Tom Cruise |
E138735
|
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: Tom Cruise | Statement: [Joseph Kosinski, workedWith, Tom Cruise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Cruise Context triple: [Joseph Kosinski, workedWith, Tom Cruise]
-
A.
Tom Cruise
chosen
Tom Cruise is an American actor and producer renowned for his charismatic performances in blockbuster action and science-fiction films, including the Mission: Impossible series and numerous critically acclaimed roles.
-
B.
Brad Pitt
Brad Pitt is an American actor and film producer renowned for his leading roles in major Hollywood films and for winning multiple Academy Awards.
-
C.
Scott Anthony Redford
Scott Anthony Redford is one of the children of acclaimed American actor and filmmaker Robert Redford.
-
D.
Nicolas Cage
Nicolas Cage is an American actor known for his intense and eclectic performances across action, drama, and independent films.
-
E.
Richard Gere
Richard Gere is an American actor known for his leading roles in films such as "American Gigolo," "An Officer and a Gentleman," and "Pretty Woman."
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15800e3608190bd3e1123ccc6c326 |
completed | April 16, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf22db3481909141ddef151d0341 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:55 a.m.