Triple
T30393645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amir Arison |
E773153
|
entity |
| Predicate | portraysProfessionOnScreen |
P153983
|
FINISHED |
| Object | FBI agent |
—
|
LITERAL 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: FBI agent | Statement: [Amir Arison, portraysProfessionOnScreen, FBI agent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysProfessionOnScreen Context triple: [Amir Arison, portraysProfessionOnScreen, FBI agent]
-
A.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
B.
portrayedByProfession
Indicates that an entity is depicted or represented by someone acting in a specified professional capacity.
-
C.
portrayedProfessionOfCharacter
chosen
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
-
D.
portraysInWork
Indicates that one entity depicts, represents, or plays the role of another entity within a specific creative work.
-
E.
occupationInFilm
Indicates that an entity has a specific occupation or role within the context of a particular film.
- 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_69f2248ef0a48190aa54d4d8ac3e5758 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd6a1c1c4881908090053bc359b181 |
completed | May 8, 2026, 4:44 a.m. |
| PD | Predicate disambiguation | batch_69fd696f24d8819091033afacbdaadc5 |
completed | May 8, 2026, 4:41 a.m. |
Created at: April 29, 2026, 8:02 p.m.