Triple
T12667398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Hanks as Colonel Tom Parker |
E302591
|
entity |
| Predicate | portraysCharacterNationality |
P32391
|
FINISHED |
| Object | Dutch-American |
—
|
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: Dutch-American | Statement: [Tom Hanks as Colonel Tom Parker, portraysCharacterNationality, Dutch-American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysCharacterNationality Context triple: [Tom Hanks as Colonel Tom Parker, portraysCharacterNationality, Dutch-American]
-
A.
portrayalNationalityOfActor
chosen
Indicates that an actor portrays a character of a specified nationality in a performance or work.
-
B.
portrayedByEthnicity
Indicates that an entity is portrayed or represented by someone of a specified ethnic background.
-
C.
portrayedByCharacterType
Indicates that an entity is depicted or represented by a character of a specified type (e.g., hero, villain, sidekick) in a narrative or media work.
-
D.
depictsNationality
Indicates that one entity visually represents or portrays the nationality or national identity of another entity.
-
E.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:20 p.m.