Triple
T32719756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Sutton |
E836637
|
entity |
| Predicate | portrayedRankOfCharacter |
P197333
|
FINISHED |
| Object | Sergeant |
—
|
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: Sergeant | Statement: [Frank Sutton, portrayedRankOfCharacter, Sergeant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedRankOfCharacter Context triple: [Frank Sutton, portrayedRankOfCharacter, Sergeant]
-
A.
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.
-
B.
portrayedTitleCharacter
Indicates that one entity played the main or title role character associated with another entity (such as a work or production).
-
C.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
D.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
E.
portrayedProfessionOfCharacter
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
- F. None of above. chosen
Provenance (4 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_69f34935455881909088975d79460418 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fe87b609888190913b0c3f787ecdba |
completed | May 9, 2026, 1:02 a.m. |
| PD | Predicate disambiguation | batch_69fe8731af48819092084f6f74bf052d |
completed | May 9, 2026, 1 a.m. |
| PDg | Predicate description generation | batch_69fe87b52bd4819087d6d338fe47c97c |
completed | May 9, 2026, 1:02 a.m. |
Created at: May 1, 2026, 1:11 a.m.