Triple
T1211265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Adjustment Bureau |
E26003
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object | Matt Damon |
E6366
|
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: Matt Damon | Statement: [The Adjustment Bureau, leadActor, Matt Damon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Damon Context triple: [The Adjustment Bureau, leadActor, Matt Damon]
-
A.
Matt Damon
chosen
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.
-
B.
Ben Affleck
Ben Affleck is an American actor, director, and screenwriter known for films such as "Good Will Hunting," "Argo," and for portraying Batman in the DC Extended Universe.
-
C.
George Clooney
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.
-
D.
Aaron Eckhart
Aaron Eckhart is an American actor best known for his roles in films such as "The Dark Knight," "Thank You for Smoking," and "Erin Brockovich."
-
E.
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.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bde581308190bbe30683bf6c48c3 |
completed | March 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8f6ff3048190a420ee6c92fc9c71 |
completed | March 7, 2026, 8:49 p.m. |
Created at: March 1, 2026, 7:46 p.m.