Triple
T3332244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seth Gecko |
E70058
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | George Clooney |
E11669
|
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: George Clooney | Statement: [Seth Gecko, portrayedBy, George Clooney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Clooney Context triple: [Seth Gecko, portrayedBy, George Clooney]
-
A.
George Clooney
chosen
George Clooney is an American actor, filmmaker, and activist renowned for his work in film and television as well as his humanitarian and political advocacy.
-
B.
Matt Damon
Matt Damon is an American actor, producer, and screenwriter known for his versatile performances in films such as Good Will Hunting, the Bourne series, and The Martian.
-
C.
Tom Hanks
Tom Hanks is an acclaimed American actor and filmmaker renowned for his versatile performances in films such as "Forrest Gump," "Saving Private Ryan," and "Cast Away."
-
D.
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.
-
E.
Danny Huston
Danny Huston is an American actor and director known for his character roles in films such as "The Constant Gardener," "X-Men Origins: Wolverine," and "Wonder 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb19358e48190a503af01b92273a4 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a841c608190942e040c14f560d2 |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.